• Describe the fundamentals of mathematical and logical aspects
• Outline the concepts of programming
• Illustrate the use of formal languages in computer science
• Explain basic computer network organisation

• Fundamental organisation of computer hardware and software: Motherboard, I/O Peripherals, Expansion slots and cards, application software, arithmetic-logic unit, registers, central processing unit, memory, storage devices
• Theoretical foundations of sets: Basic notation, representations and examples, membership and subsets, operations on sets, cartesian products, power sets, cardinality, infinite sets
• Introduction to relations and functions: Domain and range of a relation, one-to-one, one-to-many, many-to-one, inverse, reflexive, symmetric, transitive relations, into, onto, one-one, Bijective functions.
• Concepts of flowcharts and algorithms
• Introduction to propositional and predicate logic: Propositions, quantifiers, predicates, arguments
• Boolean algebra and logic gates: Combinatorial circuits, Boolean functions, Karnaugh map
• Number systems and their representations: Representation of integers and floating-point numbers in signed-magnitude and two’s-complements
• Trees, Graphs and their applications: Graphs, representation of graphs, paths and circuits, planar graph, Binary trees, decision trees, tree traversal, spanning trees
• Automata, grammars and languages: Finite state machines, languages and grammars, language recognition, Turing machines
• Introduction to computer networks: Network topologies, transmission media and network devices, ISO OSI stack, IP addressing

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• B. A. Forouzan, Foundations of Computer Science, 3rd Ed., 2014.
• P. Norton, Introduction to Computers, 7th Ed., Tata McGraw Hill Education, 2011.
• R. L. Graham, Donald E. Knuth, and Oren Patashnik. Concrete Mathematics: Foundation for Computer Science, 2nd Ed., Addison-Wesley Professional, 1994

• Demonstrate fundamental programming concepts
• Identify classes, objects, members of a class and relationships among them needed for a specific problem
• Solve variety of computational problems
• Create programs using fundamental concepts of object-oriented programming

• Interaction with a computer: Graphical user interface, command line interface, files and folders, using text editors / IDEs, programming principles
• Program development in Java: Programming principles, Edit-Compile-Run cycle, basic components of a Java program, syntax and semantics, data types, variables and constants, expressions, built-in classes
• Introduction to Object-Oriented Programming: Classes and objects, fields and methods, arguments and parameters, constructors, class and instance data values
• Control Flow: Sequence, selection, repetition, explicit control-flow statements
• Arrays and Collections: 1D & 2D Arrays, arrays of objects, for-each loop, passing arrays to methods, searching and sorting in arrays
• Concepts of recursion and backtracking: Recursion concepts, examples using recursion, recursion vs. iteration, recursive backtracking
• Implementing standard algorithms: String matching, counting coins, Knapsack problem, Huffman coding, activity-selection, scheduling problem
• Inheritance and Polymorphism: Principles of Inheritance, member accessibility, inheritance over accessibility, principles of polymorphism and polymorphic constructors/methods

• In-course Assessments
  • Assessment on practical records ———————– 10%
  • End-of-First Semester Practical Assessment ———– 30%
• End-of-Second Semester Practical examination ———- 60%

• C. T. Wu, An Introduction to Object-Oriented Programming with Java, 5th Ed., McGraw-Hill Education, 2009.
• P. Deitel and H. Deitel, Java How to Program, 9th Ed., Pearson Education, Inc., 2012.
• H. Schildt, Java: The Complete Reference, 9th Ed., McGraw-Hill Osborne Media, 2014.

• Explain the fundamental principles of multimedia
• Demonstrate compression techniques used in multimedia
• Discuss theories behind the multimedia components
• Design contents using multimedia technologies

• Introduction: Uses of multimedia, interaction technologies, multimedia hardware and devices
• Compression techniques in multimedia: compression basics, lossless and lossy compression techniques
• Text in multimedia: Visual representation of text, digital representation of characters
• Fundamentals of colours, colour models and dithering
• Fundamentals of images:  characteristics of images, image file formats, and image compression standards
• Digital audio: sound processing, representation of audio files
• Fundamentals of video and animation: analogue and digital video standards, video processing, video compression standards and file formats, basics of animation
• Designing multimedia contents: Development phases, multimedia authoring and tools, multimedia in the internet

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Z.N. Li and M.S. Drew, “Fundamentals of Multimedia”, 2nd Ed., 2014.
• A. Banerji; A. M. Ghosh, “Multimedia technologies”, 2010.
• T.M. Savage and K.E. Vogel, “An Introduction to Digital Multimedia”, 2nd Ed., 2013.

• Demonstrate familiarity of various algorithm design techniques and their applications
• Use different strategies to compare the performance of algorithms
• Discuss the usages of different Iterative and recursive algorithms
• Apply different algorithmic approaches and concepts for solving computational problems

• Algorithm Analysis: Informal comparison of algorithm efficiency, best, expected, and worst case behaviors, time and space trade-offs in algorithms, Asymptotic analysis (big O, little o, big Ω and big Θ notations)
• Problem-solving strategies: Iterative and recursive algorithms
• Brute-force and Greedy methods: Concepts of Brute-force and Greedy methods, applications of Brute-force and Greedy methods for solving problems (String matching, counting coins, Knapsack problem, Huffman Coding, Activity-selection, Scheduling problems)
• Searching and sorting in arrays and their complexities: Linear search, binary search, selection sort, insertion sort, bubble sort

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• T. Cormen, C. Leiserson, R. Rivest, C. Stein, Introduction to Algorithms, 3rd Ed., MIT Press, 2009.
• R. Sedgewick and K. Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011.

• Describe the fundamentals of Mathematical and Logical aspects.
• Outline the concepts of programming.
• Illustrate the use of formal languages in Computer Science.
• Explain basic computer network organisation.

• Fundamental organisation of computer hardware and software: Motherboard, I/O Peripherals, Expansion slots and cards, Application software, Arithmetic-logic unit, Registers, Central processing unit, Memory, Storage devices.
• Theoretical foundations of sets: Basic notation, representations and examples, membership and subsets, operations on sets, Cartesian products, Power sets, cardinality, infinite sets.
• Introduction to relations and functions: Domain and range of a relation, one-to-one, one-to-many, many-to-one, inverse, reflexive, symmetric, transitive relations. Into, Onto, One-one, Bijective functions.
• Concepts of Flowcharts and Algorithms
• Introduction to Propositional and Predicate Logic: Propositions, quantifiers, predicates, Proofs.
• Boolean Algebra and Logic Gates: Combinatorial Circuits, Boolean Functions, Karnaugh map and Applications.
• Number systems and their representations: Representation of integers and floating-point numbers in sign-magnitude and twos-complements.
• Trees, Graphs and their applications: Graphs, representation of graphs, paths and circuits, planar graph, Binary trees, decision trees, tree traversal, spanning trees.
• Automata, Grammars and Languages: Finite state machines, Languages and Grammars, Language Recognition, Turing Machines.
• Introduction to Computer Networks: Network topologies, transmission media and network devices, ISO OSI stack, IP addressing.

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Behrouz A. Forouzan, Foundations of Computer Science, 2013
• Peter Norton, Introduction to Computers (7e), Tata McGraw Hill Education Private Limited, 2011.
• Ronald L. Graham, Donald E. Knuth, and Oren Patashnik. Concrete Mathematics: Foundation for Computer Science(2e), Addison-Wesley Professional, 1994

• Convert scientific problems into computational frameworks
• Use a high-level programming language for problem solving
• Recognise the process of writing, testing and debugging a program
• Express competence and confidence in computational problem solving

• Familiarise with a personal computer: Graphical user interface, Command line interface, files and folders, using an editor, programming principles.
• Programming concepts: Sequential, selection (single-selection, double-selection, repetition) and repetition (counter-controlled repetition, sentinel-controlled repetition, and nested-control statements). String manipulation: substring, concatenating strings, equality of strings, finding index of character or a substring, reversing. Declaring and creating arrays, examples using arrays, multidimensional arrays.
• Develop small programs using methods and functions: Computing the average of a list of numbers, finding the min, max, and mode in a list, approximating the square root of a number, or finding the greatest common divisor.
• Introduction to data structures: Trees, stack, queue, linked lists, graphs.
• Searching and Sorting techniques: Linear search, binary search, bubble sort, selection sort, insertion sort, divide-and-conquer technique, merge sort, quick sort
• Concepts of recursion and backtracking: recursion concepts, examples using recursion: factorial, Fibonacci series, towers of Hanoi, recursion vs. iteration, recursive backtracking.
• Web development using HTML, CSS and JavaScript: Applets for drawing a string, executing an applet in a web browser, loading and playing audio clips.CSS development
• Develop small programs using GUI components and graphics: Using dialog boxes, creating simple drawings (rectangles, ovals, and arcs), colors and filled shapes, displaying text and images.

• Continuous assessment on practical records———————————30%
• Two mid-semester examinations (each of two hours duration)————-30%
• Two end-of-semester examinations (each of three hours duration)———40%

• P. Deitel and H. Deitel. Java how to program (9e), Pearson Education, Inc., 2012
• Herbert Schildt, Java: The Complete Reference (9e), McGraw-Hill Osborne Media, 2014
• http://docs.oracle.com/javase/7/docs/api/

• Explain fundamental data structures
• Choose appropriate data structures for problem solving
• Recall sorting and searching algorithms
• Demonstrate different algorithmic approaches for problem solving

• Introduction to scientific and engineering basis for comparing algorithms: Differences among best, expected, and worst case behaviors of an algorithm.
• Asymptotic Analysis of Algorithms: Asymptotic analysis of upper and expected complexity bounds, Empirical measurements of performance, Time and space trade-offs in algorithms.
• Data structures: Array, Stack, Queue, Linked List, Graphs and Trees.
• Algorithm design strategies: Iterative and recursive algorithms, Brute-force algorithms, Greedy algorithms, Divide-and-conquer, Recursive backtracking.
• Searching and Sorting algorithms, and their complexities.

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Thomas Cormen, Charles Leiserson, Ronald Rivest, Clifford Stein. Introduction to Algorithms, Third Ed., MIT Press, 2009, ISBN: 978-0262033848.
• Robert Sedgewick and Kevin Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011, ISBN-13: 978-0321573513.

• To introduce the historical background of computers and computer languages and to introduce computer architecture, Operating systems, and File structure
• To provide background knowledge in discrete mathematics essential to Computer Science

• Evolution of Computer Systems: Brief history of computer and computer languages; Introduction to Computer architecture, Operating systems, and File structure.
• Fundamentals of Computer Science: Number systems and their representations. Set theory and Boolean algebra; Propositional and Predicate Calculi; Vectors, Matrices, and Linear systems; Trees and Graphs; Formal machines; Complexity analysis; Combinatorics.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To demonstrate principles underlying object oriented programming.
• To give students experience and confidence in the use of an object oriented programming language for problem solving activities.

• Brief introduction to structured programming techniques: Concepts of variables and fundamental data types and structures; control constructs of programs; subroutines.
• Introduction to object oriented concepts: Concept of objects; Data abstraction and Encapsulation; Inheritance and polymorphism.
• Programming in object oriented language: Introduction to Java; Fundamental structures in Java; Objects and Classes; Inheritance; Interfaces and inner classes; Graphics Programming; Event Handling; User interface component with swing; Exceptions and debugging; Streams and files. Concurrent Programming.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To make students familiarize themselves with network environment and to use network for their needs.
• To enable students to use internet facilities for their studies.
• To enable students to solve problems using computers.

• 90 hours practical works scheduled by the department.

• Attendance at practical sessions ——————————————— 10%
• Continuous assessment on practical records ——————————– 20%
• Two mid-semester exams each of 30-45 minutes duration —————- 20%
• Two end semester examinations each of two hours duration ————- 50%

• To introduce concepts of networks and its usage.
• To enable students to design and create applets using Java.
• To enable students to design and create web pages using script programs and hyper media.

• Introduction to Networks: Types of networks; Network topologies; transmission media; Networking devices; Protocols of networks; Domain Name System; Types of Servers .
• Applications in Internet: Use of search engines, email, telnet, ftp and WWW.
• Web authoring: Introduction to Mark-up and script languages; Use of Hypermedia and tools; Applets.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To introduce concepts of networks and its usage.
• To enable students to design and create applets using Java.
• To enable students to design and create web pages using script programs and hyper media.

• Introduction to Networks: Types of networks; Network topologies; transmission media; Networking devices; Protocols of networks; Domain Name System; Types of Servers .
• Applications in Internet: Use of search engines, email, telnet, ftp and WWW.
• Web authoring: Introduction to Mark-up and script languages; Use of Hypermedia and tools; Applets.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• State key characteristics of a database
• Develop conceptual models for databases
• Create efficient databases
• Apply query language to create and manipulate databases

• Introduction to database concepts and architecture: File systems, database system concepts, three-schema architecture, classifications of database systems and database users
• Data Modeling: Entity Relationship model, relational model, network model, hierarchical model, object relational model, UML class diagrams.
• Relational database design: Relational model concepts, defining a relational schema from an ER diagram, basics of functional dependencies and normalization (1NF, 2NF, 3NF and BCNF)
• Developing and manipulating databases: Data development and manipulation using SQL, MySQL, PostgreSQL and MongoDB
• Relational algebra and relational calculus: Binary operations, Cartesian product, extended relational operator, tuple relational calculus and domain relational calculus
• File organization for conventional DBMS: Storage devices and their characteristics, file organization, fixed-length records, variable-length records, sequential file organization, indexed sequential access method
• Introduction to transaction management, concurrency control and recovery: Concept of transactions, concurrency in transaction processing, recovering databases from failure

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Ramez Elmasri and Shamkant B. Navathe, Fundamentals of Database Systems, 7^th Ed, Addison-Wesley, 2015.
• J. Date, An Introduction to Database Systems, 8^th Ed, Addison-Wesley, 2003
• Ramakrishnan and Gehrke, Database Management Systems, 3^rd Ed., McGraw-Hill, 2003.

• Implement appropriate data structures to manipulate data for various computational problems
• Devise programs to solve complex computational problems
• Create databases using database management systems
• Develop web based applications that interact with databases

• Designing and Implementing algorithms: Recursion, backtracking, Divide-and-conquer, and Dynamic programming.
• Fundamental data structures and their applications: Arrays, Lists, Stacks, Queues, Linked lists, Trees, and Graphs
• Database design, modeling and development: SQL (MySQL, MariaDB) and NoSQL (MongoDB, PostgreSQL)
• Develop web based applications: Web development using HTML, CSS and Scripting languages (PHP, JavaScript, JQuery, NodeJS)

• Assessment on practical records———————————–10%
• End-of-First Semester Practical Assessment———————-30%
• End-of-Second Semester Practical examination——————60%

• P. Deitel and H. Deitel, Java How To Program (Early Objects), 10^th Ed, Prentice Hall, 2014.
• R. Sedgewick and K. Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011.
• N. Karumanchi, Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles, 5^th Ed, 2016
• D. Kalemis, The Fundamental Concepts of Object-Oriented Programming, 2013.
• R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 7^th Ed, Addison-Wesley, 2015.
• D. Bartholomew, Getting Started with MariaDB, 2^nd Ed, 2015.

• Describe the objective and functions of modern operating systems
• Explain how resources (such as CPU, memory, storage, file and devices) are managed by the operating system
• Demonstrate the operations of a prototypical process manager
• Compare various techniques used for concurrency control

• Introduction to operating system: Architecture of modern operating systems (OS), evolution of OS, OS operations and functionalities, and open source OS
• Processes and Threads: Concept of process, process states, process control block, schedulers, context switch, interprocess communication, process scheduling, overview of threads, multicore programming and multithreading models
• Concurrency: Process synchronisation (race condition, critical-section problem, mutex locks, semaphores, classic problems of synchronization and monitors), deadlock (characterization, prevention, avoidance, detection and recovery)
• Memory management: Swapping, memory allocation, fragmentation, paging, segmentation, virtual memory and address translation
• Storage management: Mass Storage, host attached storage, network attached storage, storage area network and RAID
• File and I/O Device management: File organization and access, file system security, device drivers, direct memory access and interrupt handling

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• W. Stalling, Operating systems: Internals and Design Principles, 8^th Ed, Pearson, 2014.
• A. Silberschatz, P. B. Galvin, G. Gagne, Operating System Concepts, 9^th Ed, 2013.
• S. Tanenbaum and H. Bos, Modern Operating Systems, 4^th Ed, 2014.

• Analyse the correctness and the performance of complex algorithms
• Discuss the implementation of standard data structures
• Demonstrate skills in solving complex computational problems using suitable data structures
• Explain the divide-and-conquer paradigm and dynamic programming strategies and their usages

• Proof of correctness of algorithms: Contrapositive and contradiction, Induction, and Loop invariants
• Recurrence relations: Analysis of iterative and recursive algorithms (Quick sort and merge sort, etc.)
• Fundamental Data Structures: Arrays, Lists, Stacks, Queues, Linked lists, Trees, and Graphs
• Algorithm Design and Implementation Techniques: Divide-and-conquer paradigm, Dynamic programming algorithms, Recursive, and backtracking
• Applications of Trees and Graphs: Binary search, Dijkstra’s shortest path, minimum spanning tree

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, 3rd Ed., MIT Press, 2009.
• R. Sedgewick and K. Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011.
• N. Karumanchi, Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles, 5th Ed. 2016.
• S. S. Skiena, The Algorithm Design Manual, 2nd Ed., Springer, 2011.

• Discuss the software engineering principles and life-cycle
• Identify different roles played by personnel in software development and their responsibilities
• Construct software designs based on user requirements
• Apply appropriate techniques in software development, testing, maintenance, and evolution

• Introduction to Software Engineering: Software characteristics, impact of software, importance of engineering approaches, challenges and ethics in software development
• Introduction to systems analysis and design: Types of systems (transaction processing, management information, decision support, etc.), need for systems analysis and design, the system development life cycle, roles played by different personnel in system development life cycle including the role of the systems analyst
• Software development process models: Waterfall model, prototyping model, spiral model, evolutionary model, iterative model and agile methodology
• Software requirements and specifications: Types of requirements, requirement gathering processes and techniques, documenting requirements
• Software analysis techniques: Data flow diagrams, data dictionaries, process specifications and structured decisions
• Software design techniques: Object-oriented design using UML, Agile methodologies using SCRUM
• Software testing: Development testing, test-driven development, release testing and user testing
• Software maintenance and evolution: Evolution processes, program evolution dynamics, software maintenance and legacy system management

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• L. Sommerville, Software Engineering, 10^th Ed, 2015.
• E. Kendall and J. E. Kendall, System Analysis and Design, 9^th Ed, 2013.
• R. E. Beasley, Software Engineering: Principles and Practices, 2^nd  Ed, 2015.

• Describe the importance of Software Engineering
• Use appropriate techniques to gather requirements
• Create software designs based on gathered requirements
• Apply best practices in software development, maintenance and evolution

• Introduction to Software Engineering: Software characteristics, Impact of Software, Importance of Engineering approach, Challenges and Ethics
• Models of Software Process: Waterfall model, Prototyping model, Spiral model, Evolutionary model, Iteration model and Agile model
• Software Requirements and Specifications: Types of Requirements, Requirement gathering processes and techniques, Documenting requirements
• Software Design and Implementation: Object-oriented design using UML, Design patterns, Implementation issues, Open Source development
• Software Testing: Development testing, Test-driven development, Release testing and User testing.
• Software maintenance and evolution: Evolution processes, Program evolution dynamics, Software maintenance and Legacy system management

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Ian Sommerville, Software Engineering, 10th Edition, 2015
• Robert E Beasley, Software Engineering: Principles and Practices, 2nd Edition, 2015

• Demonstrate the concepts of Object Oriented Programming paradigm
• Create applications using Object Oriented Programming concepts
• Design Relational Databases using database management principles
• Develop web based applications that interact with database management systems

• Introduction to Concepts of Object Oriented paradigm: Objects, classes, methods, parameter passing, information hiding, inheritance, encapsulation and polymorphism
• Object Oriented Programming using a high level programming language
  Data management using a relational model
• Database design, modeling and development using Structured Query Language
• Develop web based applications using PHP and MariaDB

• Continuous assessment on practical records ————30%
• Two mid-semester practical examinations —————30%
• Two end-of-semester practical examinations ————40%

• P. Deitel and H. Deitel. Java How To Program (Early Objects), 10th Edition, Prentice Hall, 2014.
• D. Kalemis, The Fundamental Concepts of Object-Oriented Programming, 2013.
• R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 7th edition, Addison-Wesley, 2015.
• Daniel Bartholomew, Getting Started with MariaDB, 2nd edition, 2015.

• State key characteristics of a database
• Develop a conceptual model for given requirements
• Create an efficient relational database
• Apply Structured Query Language to create and manipulate database

• Information Management Concepts: Information capture and representation, Information storage and retrieval, supporting human needs (searching, retrieving, linking, browsing, navigating, Analysing and indexing), Quality issues (Reliability, security, scalability, efficiency) and Information management applications
• Database Systems: File systems, Evolution of DBMS, Database architecture, Data independence, DBMS user (designer, application developer, administrator), DBMS functions and system components
• Data Modeling: Entity Relationship model, Relational model, Network model, Hierarchical model and object relational model
• Relational Database Design: Schema design, mapping conceptual schema to relational schema, Normal Forms (1NF, 2NF, 3NF and BCNF)
• Structured Query Language: Basic structure, Subqueries, Set operations, Aggregate functions, Derived relations, and Views
• Relational algebra: Relational algebra, Tuple relational calculus and domain calculus

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• RamezElmasri and Shamkant B. Navathe, Fundamentals of Database Systems, 7th edition, Addison-Wesley, 2015.
• C.J. Date, An Introduction to Database Systems, 8th edition, Addison-Wesley 2003
• Ramakrishnan and Gehrke, Database Management Systems, 3rd edition, McGraw-Hill 2003.

• Outline modern operating systems
• Describe the core functionalities of an Operating System
• Demonstrate the operations of a prototypical process manager

• Introduction to Operating System: Architecture of Modern Operating Systems (OS), Evolution of OS, OS operations and functionalities, and Open source OS
• Processes and Threads: Concept of Process, Process states, Process Control Block, Schedulers, Context switch, Interprocess Communication, Process scheduling, Overview of Threads, Multicore Programming and Multithreading Models
• Concurrency: Process synchronization (Race Condition, Critical-Section Problem, Mutex Locks, Semaphores, Classic Problems of Synchronization and Monitors), Deadlock (Characterization, Prevention, Avoidance, Detection and Recovery)
• Memory management: Swapping, Memory allocation, Fragmentation, Paging, Segmentation, Virtual memory and Address Translation
• Storage management: Mass Storage, Host attached storage, Network attached storage, Storage Area Network and RAID
• File and I/O Device management: File Organization and Access, file system security, Device drivers, Direct Memory Access and Interrupt handling

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Stallings, W. Operating systems: internals and design principles, 8th edition, Pearson, 2014.
• Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, Operating System Concepts, 9th edition, 2013.
• S. Tanenbaum and Herbert Bos, Modern operating systems ,4th edition, 2014.

• Apply software engineering principles and practices for the planning and the development of a software product
• Practice as an effective player of a software project team
• Create professional-quality deliverables
• Demonstrate abilities to manage pressures and procedures of a team work in an industrial setup

• This course unit introduces and applies a range of topics in software engineering in the context of a team project
• Students will be assigned to a group of three to four members and each group works to specify, design, implement, and document a software project
• The course unit is oriented around directed and self-paced learning, supported by weekly mentoring and discussions

• Group Project Report —————– 40%
• Software product ———————- 30%
• Project Presentation —————— 10%
• Individual viva voce —————— 20%

• Explain the structure of complex data structures, their implementation, the algorithms that manipulate them and their amortized analysis
• Develop efficient algorithms to solve complex problems
• Evaluate complexity of algorithms
• Demonstrate skills with applied algorithmic settings on operational research, parallel and distributed computing, and operating systems

• Proof of correctness of algorithms: Inductive proofs and loop invariants
• Divide-and-conquer paradigm: Sorting and Searching algorithms that use this paradigm, synthesize divide-and-conquer algorithms, Derive and solve recurrences describing the performance of divide-and-conquer algorithms
• Abstract data types: Implementation of Trees and Graphs, Dijkstra’s Algorithm, Depth-First vs Breadth-First Searches and their complexity
• Greedy approach: Principles of Greedy Algorithms and their applications, Synthesize greedy algorithms, and analyze them

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Helen Sharp, Yvonne Rogers, and Jenny Preece. Interaction Design: Beyond human-computer interaction, 4th ed., Wiley Publishers, 2015.
• Ben Shneiderman, Catherine Plaisant, Maxine Cohen and Steven Jacobs. Designing the User Interface: Strategies for Effective Human-Computer Interaction, 5th ed., Addison Wesley publishers, 2009.

• Explain the concepts of number representation and error analysis in digital computers
• Apply numerical methods to solve systems of linear, nonlinear and differential equations
• Use Curve fitting, Interpolation and Polynomial approximation techniques

• Numerical Representation and Error Analysis: floating-point arithmetic, Precision and Accuracy, rounding rules, Absolute and Relative error, and computational error analysis
• Solving Systems of Linear Equations: Eigen values, Eigen vector, vector and matrix norms, LU decomposition, Gaussian elimination, Pivoting, Jacobi method, Gauss-Seidel method
• Solving Systems of Nonlinear Equations: Bisection, false-position, incremental searches, Fixed-point iteration, Newton-Raphson, Secant method
• Curve fitting, Interpolation and Polynomial Approximation: Lagrange interpolating polynomials, Newton interpolating polynomials, cubic and natural spline interpolation, convergence and error bound for polynomial interpolation of a continuous function
• Numerical Integration and Differentiation: Newton-Cotes quadrature, Trapezoidal rule, Simpson’s rule, Romberg integration, and numerical differentiation

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• K. Sankara Rao: Numerical Methods for Scientists and Engineers, 2008
• George W. Collins: Fundamental Numerical Methods and Data Analysis, 2003.

• State various types of threats and attacks, and countermeasures
• Describe various measures to avoid vulnerabilities in programs
• Analyse network and platform to detect threats
• Perform basic digital forensics

• Threats and Attacks: Types of Attacks, and Attacker goals, capabilities and motivations.
• Defensive Programming: Input validation and data sanitization, Handling of exceptions and unexpected behaviours
• Network specific threats and attacks: Denial of Service, spoofing, sniffing and traffic redirection, man-in-the-middle, message integrity attacks, routing attacks, traffic analysis, architectures for secure networks, and defense mechanisms and countermeasures
• Platform Security: Code integrity and code signing, Secure booting, TPM and secure coprocessors, Attestation, and Security of embedded devices
• Digital forensics: Principles and methodologies, attack detection and investigation

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• William Stallings, Cryptography and Network Security: Principles and Practice, 6th edition, Pearson, 2013.
• Michael Jang, Security Strategies in Linux Platforms and Applications (Information Systems Security & Assurance), 1st edition, Jones & Bartlett Learning, 2010.
• Charles P. Pfleeger and Shari Lawrence Pfleeger, Analyzing Computer Security: A Threat / Vulnerability / Countermeasure Approach, 1st Edition, Prentice Hall, 2011

• Explain the fundamental principles of multimedia
• Demonstrate compression techniques used in multimedia
• Discuss theories behind the multimedia components

• Introduction: Uses of Multimedia, Interaction Technologies, Multimedia Hardware and Devices
• Compression Techniques in Multimedia: Compression Basics, Lossless and lossy compression techniques
• Text in Multimedia: Visual Representation of Text, Digital representation of characters
• Fundamentals of Colours, Colour Models and Dithering
• Fundamentals of Images: Characteristics of Images, Image file formats, and Image compression standards
• Digital Audio: Sound Processing, Representation of Audio files
• Fundamentals of Video and Animation: Analogue and Digital Video Standards, Video Processing, Video compression standards and file formats, Basics of animation
• Designing Multimedia: Development Phases, Multimedia Authoring and Tools, Multimedia in Internet and World Wide Web.

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Ze-Nian Li and Mark S. Drew, “Fundamentals of Multimedia”, Second Edition, 2014
• Ashok Banerji; Ananda Mohan Ghosh, “Multimedia technologies”, 2010
• T.M. Savage and K.E. Vogel, “An Introduction to Digital Multimedia”, Second Edition, 2013.

• Appraise concepts, methods and tools used in Bioinformatics
• Demonstrate the fundamental computational knowledge required to tackle problems in bioinformatics
• Develop computational applications in bioinformatics using computer algorithms

• Introduction to Bioinformatics: aims and tasks of Bioinformatics, history and scope of bioinformatics and its applications, bioinformatics databases.
• Structural Bioinformatics: protein structure basics, protein structure visualization, comparison and classification, protein secondary structure prediction, protein tertiary structure prediction, RNA structure prediction.
• Sequence Alignment: pairwise sequence alignment, multiple sequence alignment, Hidden Markov Models.
• Pattern Recognition: Clustering and Visualisation
• Genomics and Proteomics: genome mapping, genome assembly, genome comparison, functional genomics, proteomics and metabolomics

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• B. Bergeron, Bioinformatics Computing, Prentice Hall, 2002.
• Krawetz. Stephen A: Introduction to Bioinformatics: A Theoretical and Practical Approach, 2003
• F. Azuaje and J. Dopazo, Data Analysis and Visualization in Genomics and Proteomics, John Wiley, 2005

• To understand basics of algorithms, concepts of time and space complexity in worst, average and best cases, and the asymptotic behaviour and order of magnitude of functions.
• To be able to develop efficient algorithms for simple computational tasks and to compute complexity measures of algorithms, including recursive algorithms using recurrence relations.
• To know and apply a range of algorithm design techniques.
• To know and understand a wide range of searching and sorting techniques and to implement them
• To be familiar with several data structures and to get to know their constructions and uses.

• General concepts : Introduction to Algorithms and Mathematical background – Time and space Complexity, Asymptotic Analysis; Average, best and worst case analysis, recurrence relations and their use in algorithm analysis
• Data structures: Introduction and Implementation of Stacks and Queues, Linked Lists, Hash Tables, and Trees, and their applications
• Algorithm design techniques: Greedy methods, Divide and conquer methods, Dynamic programming, Recursion, Influence of data structure on algorithm performance
• Graphs and digraphs: Representations, Breadth and depth first searches, Shortest path, Minimal spanning tree, Hamiltonian path and travelling salesperson problems etc.
• Sorting: selection, insertion, bubble sort. Quicksort, mergesort, heapsort , Bucket sorting, Worst case and average behaviour, Order statistics

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Two Short exams based on tutorials (10-15 minutes each) ————– 10%
  • Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To give principles and practical solutions for storage and retrieval of information.
• To emphasis both on the use and on the implementation of database management systems, enabling students in both logical and physical database design and complex query execution using standard query languages.
• To introduce the modern databases for multimedia and spatial requirements.
• To familiarize with a broad range of data management issues including data integrity, security, transaction and concurrency.

• Introduction to Database: Evolution of Database technology, Data models, three level schema architecture, Database languages, Physical file structures and Indexing techniques.
• Database design: ER model, Relational Model, Normalisation – First, Second, Third and Boyce-Codd normal forms.
• Relational algebra and calculus: Query languages, Relational algebra operations, relational algebra query expressions, tuple calculus and domain calculus query expressions.
• Introduction to SQL: Basic structure, Sub queries, Set operations, Aggregate functions, Derived relations, Views.
• Advanced Features: Transaction, concurrency control and recovery; Security levels, Integrity constraints,
• Modern databases: Object Oriented Databases and Relational object Database design, Persistence Objects, Query languages.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Two Short exams based on tutorials (10-15 minutes each) ————– 10%
  • Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To offer a foundation in programming skills for popular languages and platforms.
• To systematically address theories and methods of typical software engineering processes and models governed by industry standards.
• To emphasize the development of reliable and maintainable software via system requirements and specifications.
• To enable students capable in software design methodologies including object-oriented design implementation, integration, and testing.

• Introduction to Software Engineering: Software characteristics, Software applications, software process models.
• Project Management: Project planning, risk management, human interactions in software development.
• Analysis principles: Analysis modelling, design methods, software testing methods
• Computer Aided Software Engineering: Taxonomy of CASE tools, application of case tools, integrated case environment.
• Client-Server Software Engineering: Structure and usage of client-server systems, distribution of software component.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Mini project (implementation + presentation) ————————— 30%
• End-of-course Examination ————————————————– 60%

• To enable to implement several data structures and algorithms
• To enable to be familiar with designing and creation of databases and to manipulate them using query languages.
• To enable to efficiently find solutions to numerical and non-numerical problems.
• To enable to be familiar with programming in logic.

• 90 Hours practical works scheduled by the Department.

• Attendance at practical sessions ——————————————— 10%
• Continuous assessment on practical records ——————————– 20%
• Two mid-semester exams each of 30-45 minutes duration —————- 20%
• Two end semester examinations each of two hours duration ————- 50%

• To enable the students to gain basic knowledge in numerical methods.
• To learn a programming language and tools suitable for numerical computing.
• To develop their programming skill in solving a variety of numerical problems.

• Data Representation and Computational Error: floating-point numbers, rounding rules, machine precision, floating-point arithmetic; well-posed problems, problems vs. algorithms, data error vs. computational error, forward error vs. backward error, conditioning of a problem, stability of an algorithm.
• System of Linear Equations: vector spaces, matrices and other relevant matters; vector norm, matrix norm, condition number of a matrix; LU factorization, Gauss-Seidel elimination, Error estimation; refinement of special cases.
• System of Non Linear Equations: Simple vs. multiple root; bisection, fixed-point iteration, Newton-Raphson method, secant method; fixed-point iteration, Jacobian matrix, Newton’s method; convergence rate and order analysis.
• Interpolation and Polynomial Approximation: Lagrange and Newton forms, Hermite interpolation, cubic and natural spline interpolation; convergence and error bound for polynomial interpolation of a continuous function; Chebyshev polynomials; Weierstass Approximation Theorem, best uniform approximation, least squares approximation.
• Numerical Integration and Differentiation: Newton-Cotes quadrature, Trapezoid rule, Simpson’s rule; Romberg integration.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Mini project (implementation + presentation) ————————— 30%
• End-of-course Examination ————————————————– 60%

• To explain the fundamentals of simple predicate logic and its use for knowledge representation and deduction.
• To understand the fundamentals of logic programming, in particular the sources of non-determinism, the significance of displays of computational spaces and the significance of language semantics.
• To demonstrate familiarity with the language Prolog and its practical use.

• Formal Logic: revision of Propositional and First Order Predicate Logic, conjunctive normal form, clausal form, knowledge representation by clausal form.
• Logic Programming: basic concepts, the resolution rule of inference, application strategies, pattern matching, backtracking, computational space – total and standard, non-determinism, procedural and declarative semantics.
• Prolog: Horn Clauses, terms and terminology, built-in predicates, non-determinism and input/output, the cut, negation, list processing, abstract data types.
• Applications: Realising the suitability of Logic Programming in solving problems, Identifying problems that can be solved by Logic Programming, Conversion of problems in to those suitable for Logic programming, Implement solving methods to find solutions.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Mini project (implementation + presentation) ————————— 30%
• End-of-course Examination ————————————————– 60%

• Describe the concepts of software development methodologies
• Demonstrate the importance of Rapid Application Development (RAD) and its key elements
• Discuss how systems analysts interact with users, management, and other information systems professionals for gathering requirements
• Analyse the development lifecycle of a given software project
• Develop a software rapidly by best practices and tools

• Introduction to RAD: Issues with traditional software development, advantages and disadvantages of RAD practices, pillars of RAD
• Key elements of RAD: Teamwork, risk management, project scheduling, project estimation
• Agile Software Development: Agile manifesto, agile methodologies such as SCRUM, extreme programming, Lean, and Kanban, Agile vs waterfall model
• Information Requirements Analysis: Determining system requirements, Interactive information gathering methods such as interviewing, Joint Application Development and Questionnaires. Unobtrusive information gathering methods such as sampling and investigation.
• Analysis Process: Data flow diagrams, analysing systems using data dictionaries, process specifications and structured decisions, designing effective input and output, designing databases
• Testing: Fundamentals of testing, black-box testing techniques, white box testing techniques, levels of testing, test cases
• Quality Assurance and Implementation: Ensuring data quality, six sigmas, quality assurance through software engineering, implementing information system, software testing process, evaluation techniques
• Best Practices and Tools: Software architectural patterns, software design patterns and software version control (SVC)
• Software Project Management: Work breakdown and cost estimation, Break-even analysis, cash-flow analysis, present value analysis, project scheduling using Gantt chart and PERT diagrams

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• A. Stellman and J. Greene, Learning agile: Understanding Scrum, XP, lean, and kanban. O’Reilly, 2014.
• S. McConnell, Rapid development: Taming wild software schedules, Pearson Education, 1996.
• J. Loeliger, Version Control with Git, O’Reilly Media, 2012.
• E. Kendall and J. E. Kendall, System Analysis and Design, 9th Ed, Pearson, 2013.

• Apply rapid development methodologies used in the software industry
• Create software applications using development frameworks
• Practice application programming interface (API) for computer graphics
• Make use of computer graphics algorithms for its applications
• Write socket programming using Python libraries
• Setup web servers to enable interactions with other web servers using network protocols
• Demonstrate skills to configure, administer and secure local area network devices
• Administer Linux based systems

• Version Control Systems: Introduction to GitHub and its workflow, branching, merging pull requests, working with teams on GitHub, creating task lists
• Development frameworks: Introduction to frameworks such as Laravel, setting up and install Laravel framework
• Software Applications: View/Session/Application management, databases in web application with Laravel
• Socket programming using Python: Client-server and TCP/UDP programming, multithreaded proxy server, reliable transport layer programs, distributed programs to implement routing algorithms, open and proprietary network applications development
• Network Design, Management and Troubleshooting: Setting up LAN, configuring and managing devices such as switches and routers with access controls, IP address configurations and troubleshooting
• Host Administration with LINUX: Basic commands, files, directories and file system, editors, processes, users and group management, package management, shell scripts
• OpenGL: Construction of interactive user interfaces, fundamentals of 2D and 3D graphics
• Computer Graphics Algorithms and Methods: Object modelling and representation, mapping and clipping, 2D and 3D transformations, rendering for visual realism

• In-course Assessments (Practical) —————————————20%
• End-of-Semester Practical Examination ———————————30%

Semester-2

• In-course Assessments (Practical) —————————————20%
• End-of-Semester Practical Examination ———————————30%

• M. Stauffer, Laravel: Up and Running: A Framework for Building Modern PHP Apps, O’Reilly Media, 2019.
• J. Kurose and K. Ross, Computer Networking: A Top-Down Approach, 7th Ed., Addison Wesley, 2017.
• S. Guha, Computer Graphics Through OpenGL: From theory to experiments 3rd Ed., CRC, 2019.
• W. Odom, CCNA Routing and Switching 200-125 Official Cert Guide Library, 1st Ed., Cisco Press, 2016.

• Describe data communication principles, layered architectures and protocols
• Discuss routing and switching principles, and algorithms
• Distinguish Local Area Network (LAN) standards, topologies, hardware and their selection criteria for enterprise usage
• Formulate network services and applications by taking into account of quality of service, scalability and maintenance

• Fundamentals of Digital Communications: Design issues related to data transfer, compare and contrast the circuit and packet switching technologies, multiplexing techniques and error control mechanisms
• Network Architectures: Principles of layered architecture, roles of layers in the OSI and TCP/IP models
• Internet Protocols: Application layer protocols in the Internet, TCP/IP protocol suite, transport and network protocols with an emphasis on TCP/IP model, IP addressing and subnetting, troubleshooting in IP networks, IP routing mechanisms, IP versions 4 and 6, MAC layer and its protocols
• Routing and Switching: Routing and switching fundamentals, router architecture, routing algorithms, issues to consider in designing routing protocols
• LAN: Technologies such as IEEE 802 LAN standards, channel allocation in LAN segments and solutions, Ethernet and Ethernet networking using hubs and switches, problems associated in deploying wireless LANs and solutions

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• J. F. Kurose and K. W. Ross, Data Communication and Computer Networks: A top-down approach, 7th Ed. Addison Wesley, 2017.
• A. S. Tanenbaum, and D. J. Wetherall, Computer Networks, 5th Ed., Pearson Education, 2011.
• L. L. Peterson and B. S. Davie, Computer Networks: A Systems Approach, 5th Ed., Morgan Kauffman, 2011.

• Apply software engineering principles and practices for the planning and development of a software product
• Practice as an effective player of a software project team
• Use appropriate tools, principles and best practices for developing an application
• Create professional-quality deliverables
• Develop an application based on a given set of requirements in order to deploy the application at the client site
• Demonstrate abilities to manage pressures and procedures of a team work in an industrial setup

• This course unit introduces and applies a range of topics in software engineering and rapid application development in the context of a team project
• Students will be assigned to a group of three to four members and each group works to specify, design, implement, and document a software project
• The course unit is oriented around directed and self-paced learning, supported by weekly mentoring and discussions

• Team Software Project Report ——————————————-30%
• Software product and deployment of the software ——————–40%
• Project Presentation and individual viva-voce ————————–30%

• Discuss the fundamental concepts in computer graphics
• Describe the standard methods in object modelling and representation
• Apply transformation functions to animate 2D and 3D objects on view-planes
• Use rendering methods and algorithms to create photo-realistic interactive scenes from 2D and 3D models

• Fundamental Concepts: Applications of computer graphics, image representations, vector vs. raster graphics, colour models
• Object Modeling and Representation: Rasterization of lines and circles, parametric forms of curves and surfaces, solid modelling with polygonal meshes
• Mapping and Clipping: Window to viewport mapping, algorithms for clipping lines, and polygons
• 2D and 3D Transformations: Affine transformations in 2D and 3D, coordinate transformations, view plane and view volume, projections, viewing transformation
• Basic rendering for visual realism: Visibility and occlusion (such as depth buffering, Painter’s algorithm, and ray tracing), polygon filling, texture mapping, and shading models

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• S. Marschner, and P. Shirley, Fundamentals of Computer Graphics, CRC Press, 4th Ed., 2015.
• S. Guha, Computer Graphics Through OpenGL: From Theory to Experiments 3rd Edition, CRC, 2019.
• D.D. Hearn, M.P. Baker, and W. Carithers, Computer Graphics with OpenGL, 4th Ed., 2010.
• S.J. Gortler, Foundations of 3D Computer Graphics, MIT Press, 2012.

• Discuss the fundamental concepts in computer graphics
• Use a standard Application Programming Interface (API) for the applications in Computer Graphics
• Apply transformation functions to animate 2D and 3D objects on view- planes
• Write programs to implement standard algorithms in computer graphics
• Construct photo-realistic images or interactive scenes from 2D and 3D models

• Introduction to computer graphics: Image representations, vector and raster graphics, colour models, applications of computer graphics
• OpenGL: Construction of interactive user interfaces, 2D and 3D Fundamentals
• Mapping and clipping: Window to viewport mapping, algorithms for clipping lines and polygons
• Transformations and viewing: Affine transformations in 2D and 3D, coordinate transformations, view-plane and view-volume, projections, viewing transformation
• Geometric Modeling: Parametric form of 2D primitives such as lines and circles, representation of curves and surfaces
• Basic rendering for visual realism: Visibility and occlusion such as depth buffering, Painter’s algorithm, and ray tracing; Polygon filling, texture mapping, and shading models
• Implementation of standard algorithms of computer graphics

• In-course Assessments —————————-20%
• End-of-course Practical Examination ———– 30%
• End-of-course Theory Examination ————- 50%

• Steve Marschner, Peter Shirley, Fundamentals of Computer Graphics, CRC Press, (4th Edition), 2015.
• Sumanta Guha, Computer Graphics Through OpenGL: From Theory to Experiments (2nd Edition), 2014.
• Donald D. Hearn, M. Pauline Baker, Warren Carithers Computer Graphics with OpenGL (4th Edition), 2010.
• Steven J. Gortler, Foundations of 3D Computer Graphics (MIT Press), 2012.

• Use the fundamentals of logic programming with the significance of language semantics
• Write programs in Prolog for solving logical problems
• Develop .Net applications using best practices in programming
• Construct web services and integrate them to .Net applications

• Introduction to Prolog: Syntax, interpreter, matching, simple programming
• Arithmetic operations and list processing: Arithmetic and logical operations, the member and append predicates in list, and list operations
• Backtracking, cut, and negation: The cut operator for controlling backtracking, negation as failure and its uses
• Introduction to .NET Framework: .Net programming concepts and object-oriented programming
• Windows-based applications: Forms, controls and ADO.NET
• NET for web applications: View/Session/Application management, databases in web application with ADO.NET
• Building and Exposing XML Web services: Remoting, integrating Web Services and consuming data objects in web application

• Continuous assessment on practical records ———————–20%
• Mid-semester practical examination   —————————— 30%
• End-of-course practical examination ——————————- 50%

• Bratko, I., PROLOG programming for artificial intelligence. Addison-Wesley, 4th Ed., 2001.
• Clocksin, W., and Mellish, C.S., Programming in Prolog: Using the ISO Standard, 5th Ed., 2003.
• Joseph Albahari and Ben Albahari, C# 6.0 in a Nutshell: The Definitive Reference, 6th Edition, O’Reilly Media, 2015.
• Jamie Kurtz and Brian Wortman, ASP.NET Web API 2, 2nd edition, Apress, 2015

• Describe the importance of Rapid Application Development methodologies
• Appraise the importance of teamwork, risk management, scheduling and estimation in Rapid Application Development
• Develop a software rapidly by choosing a suitable agile methodology, best practices and tools

• Introduction to Rapid Application Development (RAD): Issues of traditional software development; evolution, constraints and characteristics, advantages and disadvantages of RAD practices
• Key elements of RAD: Teamwork, risk management, project scheduling, project estimation
• Agile Software Development: Agile software development and its tools, agile methodologies: Scrum, Extreme Programming, Lean, Kanban
• Best practices and tools: Software architectural patterns, software design patterns and Software Version Control

• In-course Assessments —————————- 30%
• End-of-course Examination ———————- 70%

• Andrew Stellman and Jennifer Greene, Learning agile: Understanding scrum, XP, lean, and kanban. O’Reilly Media, Inc., 2014.
• Steve McConnell, Rapid development: taming wild software schedules, Pearson Education, 1996.
• Erich Gamma, Design patterns: elements of reusable object-oriented software. Pearson Education India, 1995.
• Jon Loeliger, Version Control With Git, O’Reilly Media, 2012.

• Use appropriate tools, principles and best practices for developing an application rapidly
• Practice as an effective player of a software project team
• Develop an application based on a given set of requirements and deploy the application at the client site

• This course unit introduces and applies a range of topics in Rapid Application Development
• Students can form a group of three to four members and each group works to develop an application rapidly for a given context without compromising the user experience and the user satisfaction
• The course unit is oriented around directed and self-paced learning, supported by weekly updates, mentoring and discussions

• Continuous updates and Use of an Agile practice————————– 20%
• Project Report   —————————————————————- 20%
• Final Project Presentation and Demonstration —————————- 40%
• Individual viva-voce ———————————————————-  10%
• Deployment of the software ————————————————– 10%

• Erich Gamma, Design patterns: elements of reusable object-oriented software. Pearson Education India, 1995.
• Jon Loeliger, Version Control With Git, O’Reilly Media, 2012.
• Semmy Purewal, Learning Web App Development: Build Quickly with Proven JavaScript Techniques, O’Reilly Media, 1st edition, 2014.
• Josh Lockhart, Modern PHP: New Features and Good Practices, O’Reilly Media, 1st Edition, 2015.

• Depict knowledge using different knowledge representation techniques
• Compare various schemes used in logic-based knowledge representation
• Develop logic programs with the significance of language semantics
• Appraise the unique perspective Prolog gives to problem solving and algorithm design

• Introduction to knowledge-based technologies and knowledge representation: Approaches to knowledge representation, issues on knowledge representation
• Representation of problems and inference using predicate calculus
• Automatic theorem proving: The principle of resolution, skolemization, unification, the Horn clause, logic programming
• Representing knowledge using rules: Procedural versus declarative knowledge, forward versus backward reasoning
• Representing knowledge using slot-and-filler structures: Semantic nets, frames, conceptual dependency diagram, scripts
• Prolog: Declarative representation, inference control, programming and metaprogramming, Constraint Programming (CP), methods and tools,  Answer Set Programming (ASP)

• In-course Assessments —————————- 30%
• End-of-course Examination ———————- 70%

• Frank van Harmelen and Vladimir Lifschitz, Handbook of Knowledge Representation, Elsevier Science, 2008.
• Russell, S. and Norvig, P. Artificial Intelligence: A Modern Approach, 3rd Edition, Pearson, 2010.
• Brachman, R. and Levesque, H., Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.

• Design and configure a Local Area Network for an organisation
• Perform system and network operations
• Configure a server environment consisting web server, file server and proxy server using best practices
• Demonstrate ability to administer and secure servers, and Local Area Networks

• Network design: Cabling, network protocols, IP addressing, preparing Bill of Materials
• Host administration: Basic commands, Files, Directories and File System, Editors, Processes, Users and group management, Package management, shell scripts.
• Network administration: Host Network configuration, Switch configuration, Wireless equipments, VLAN and setting up Local Area Networks.
• Server and network administration: Setup Web Server, file server and Proxy server, administering servers and computer networks, system and network security, backups.

• Continuous assessment on practical records —————————- 30%
• End-of-semester theory examination ———————————— 30%
• End-of-semester practical examination ——————————— 40%

• Adam Haeder, Stephen Addison Schneiter, Bruno Gomes Pessanha, James Stanger, LPI Linux Certification in a Nutshell, 3rd Edition, O’Reilly Media, 2010.
• Brain Ward, How Linux Works: What Every Super user Should Know, 2nd edition, No Starch Press, 2014.

• To introduce the concepts underlying Operating Systems, the major services provided and how different choices in Operating System Design and Implementation effects the application programmer’s and user environments.

• Introduction: Role of an operating system. Abstract view of an operating system development. Evolution of hardware and operating systems, multi programming, time sharing.
• Process Control and Scheduling: processes, threads, process states, context switching, scheduling algorithms.
• Process Synchronization: Problem with unrestricted concurrency, Race conditions and critical sections, Mechanisms for process synchronization, classical problems.
• Memory Management: Allocation strategies for main memory, fragmentation, fixed and dynamic partitioning strategies. The buddy system. Swapping and relocation. Address translation, page tables and paging, Memory protection and multilevel paging mechanisms, Page problem of shared pages.
• File Systems: Definition and purpose of a file system. Files and file attributes, operations on files, files as generic interfaces for I/O channels. Access methods, directories and directory organization. Operation on directories, file allocation policies and storage management strategies, free space management.
• Deadlocks: Definition of deadlock, conditions for deadlock. Safe and unsafe system states, resource allocation graphs, Methods for prevention, avoidance, and detection. Safe states. The Bankers Algorithm.
• Access Control and Security: Domains of protection. Domain structure, domain implementation in UNIX, Access Control Lists and Capabilities, The safety problem, the confinement problem.
• Device I/O: Device drivers: character and block mode devices, polling vs. interrupts: Interrupt handling, vectored interrupts, DMA, Buffer allocation and management.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To understand the relevance and underlying infrastructure of the multimedia.
• To understand core multimedia technologies and standards.
• To be aware of factors involved in multimedia systems performance, integration and evaluation.

• Introduction: Multimedia applications and requirements (e.g., overview of multimedia systems, video-on-demand, interactive television, video conferencing, hypermedia courseware, groupware, World Wide Web, and digital libraries).
• Audio/Video fundamentals: Analog and digital representations, human perception, and audio/video equipment, applications.
• Audio and video compression: Perceptual transform coders for images/video, scalable coders and perceptual audio encoders. Application and performance comparison of various coding algorithms including hardware/software trade-offs. Image and video processing applications and algorithms.
• Multimedia hardware and software: Computer architecture and devices, OS support, network protocols, multicast networks (MBONE), continuous media system services and toolkits, and digital media servers and storage systems.
• Content authoring tools: image/video/audio editors, asset management database systems, web authoring tools, scripting languages, web/database tools, automatic web/page generation.
• Advanced multimedia: Virtual reality, Augmented reality, Multimedia databases/digital libraries Multimedia systems: Case studies (e.g., galleries, simulated environments, documents, educational courseware)

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To offer a foundation in Rapid Application concepts and methodology, advantages and limitations of RAD.
• To enable to acquire knowledge sufficient to undertake a moderately complex systems development project using RAD tool.

• RAD concept and Introduction to programming task: RAD phases, comparison between RAD and traditional methodologies, introduction to the latest tools available, introduction to a RAD environment.
• Dialog boxes, SDI, MDI applications.
• Component development: introduction to COM, DCOM, DLLs, VBXs, OCXs, Active X and other technologies such as Java Beans, RMI, component based development, re usability.
• Database design and Modelling tools: DAO, RDO, ADO technologies, Client/Server Architecture, Data service, Reverse Engineering, Round trip engineering.
• Internet Integration and Network Communication: Adding Web browsing, network and socket programming, Client/Server debugging.
• Project: using RAD Tools etc.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Individual Project (Implementation + Presentation) ———————- 40%
• End-of-course Examination ————————————————— 50%

• To introduce the principles, methods and programming languages used in the World Wide Web.

• Introduction to Internet Programming: Technologies for web applications, HTML, SGML, extending functionality of the browser.
• Client-server interaction: HTTP-HyperText Transfer Protocol, SHTTP-Secure HTTP, Server side programming, CGI-Common Gateway Interface, SSI-Server Side Includes.
• Server side programming: CGI, PERL-Practical Extraction Report Language, Environment variables of CGI, Special PERL variables, String processing and regular expressions in PERL, HTML forms and CGI, Manipulating files in PERL.
• Client side programming: DOM-Document Object Model, PHP, Using JavaScript with forms Java, DHTML-Dynamic HTML
• Web services: introduction to XML, XML applications, XHTML-Extensible HTML, XML and Java, SOAP, WSDL and UDDI.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To enable to be familiar with designing and creating dynamic web pages with different techniques.
• To enable to program some algorithms used in Operating Systems using a high level language.
• To enable to efficiently implement different algorithms used in computer graphics.

• 90 hours practical works scheduled by the Department.

• Attendance at practical sessions ——————————————— 10%
• Continuous assessment on practical records ——————————– 20%
• Two mid-semester exams each of 30-45 minutes duration —————- 20%
• Two end semester examinations each of two hours duration ————- 50%

• To provide background knowledge in the graphics concepts, method and algorithms.
• To enable students to implement applications, user interfaces and software packages using computer graphics concepts and algorithms.

• Introduction to Computer Graphics: concept of graphics, computer graphics applications, colour representation (grey scale representation, colour models and representation).
• Computer graphics systems: computer graphics hardware (CRTs, Raster graphics devices, vector graphics devices, input devices), computer graphics software.
• Two dimensional graphics primitives: scan conversion of points and lines, line drawing algorithms, circle drawing algorithms, filled areas drawing algorithms (Flood fill, boundary fill algorithms).
• Two dimensional geometric transformations: basic transformations, matrix representations and homogenous coordinates, other transformations (shear, reflection), composite transformations.
• Two dimensional viewing: the 2D viewing pipeline, windows, viewports, window to viewport mapping, clipping operations.
• Three dimensional graphics concept: three dimensional coordinate system, three dimensional display methods, hidden lines and surface removal (Z-buffer algorithm).
• Three dimensional geometric transformations: translation, scaling, rotation, skewing, reflections, composite transformations.
• Three dimensional viewing: viewing pipeline, viewing coordinates, projections (Parallel and Perspective).
• Bezier curves, B-spline curves, NURB curves.

• In-course Assessments
• Attendance at lectures and submission of tutorials ——————— 10%
• Two Short exams based on tutorials (10-15 minutes each) ————– 10%
• Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• Describe the concepts of database & distributed database design, and their logical & physical organisations
• Design a database using standard practices and tools
• Develop advanced queries to handle information retrieval from databases
• Explain the concepts of transaction process, concurrency control, and recovery mechanisms
• Discuss new developments in database technologies and the impacts of emerging database standards

• Relational Modeling: Concepts of data modeling, enhanced entity-relationship(EER) model, use of unified modeling language (UML), higher level normalisation
• Physical Organisation of Databases: Storage and file structure, indexing, database efficiency and tuning
• Query Optimisation: Factors governing query optimization, centralized query optimization
• Transaction: Transaction processing, concurrency control, recovery techniques
• Distributed Database Management Systems: Data fragmentation, replication and allocation, transaction processing, concurrency control and recovery in distributed databases
• Advanced DBMS Concepts: Advanced technologies in DBMS and enhanced data models

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• R. Elmasri and S.B. Navathe, Fundamentals of Database Systems, 7th Ed., Addison-Wesley, 2015.
• C.J. Date, An Introduction to Database Systems, 8th Ed., Addison-Wesley, 2003.
• Ramakrishnan and Gehrke, Database Management Systems, 3rd Ed., McGraw-Hill, 2003.

• Demonstrate a deeper understanding of modern computer networks, applications, and network services
• Explain how different networking technologies at the same or different layers interact and affect each other in a large-scale system
• Appraise network technologies with respect to system requirements, based on information from recent research and technical documentation
• Perceive trends in large scale networks such as virtualization and software defined networking capability
• Evaluate network technologies, applications, and services through simulation and emulation experiments

• Routing in the Internet: Intra and interdomain routing, unicast and multicast routing protocols, traffic classes and measurements, multi-protocol label switching(MPLS), storage area networks (SAN), data and the control planes
• Fundamental Properties of Computer Networks: Congestion control, queueing and scheduling, quality of service, quality of experience, Power laws
• Software Defined Networks: Distinguish between traditional networks and software defined networks, SDN origins and evolution, centralized and distributed control and data planes, openflow protocol, SDN controllers, Mininet, NOX/POX, network programming using SDNs
• Network Virtualization: Introduction to network virtualization, constructing virtual network topologies on top of physical network topologies, virtual machines, architectural issues
• Network Measurement: Measurement, modeling and analysis methods using real network data, Wireshark tool to monitor active networks, network simulations and emulations

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• J. F. Kurose and K. W. Ross, Data Communication and Computer Networks: A top-down approach, 7th Ed. Addison Wesley, 2017.
• A. S. Tanenbaum, and D. J. Wetherall, Computer Networks, 5th Ed., Pearson Education, 2011.
• L. L. Peterson and B. S. Davie, Computer Networks A Systems Approach, 5th Ed., Morgan Kauffman, 2011.

• Formulate an efficient Intelligent system model for a problem expressed in natural language
• Use knowledge representation for theorem proving based on resolution procedure
• Apply appropriate uninformed, informed or local search algorithms for solving problems
• Develop logic programs with the significance of language semantics
• Devise a plan of action to achieve a goal using standard AI methods
• Illustrate the working of natural language processing techniques

• Introduction: Practical examples of artificial intelligence, intelligent agents, environments, intelligent behaviour, rational behaviour & Turing test
• Problem Solving by Searching: Problem-solving agents, uninformed search strategies, informed (Heuristic) search strategies
• Local Search and Optimization Algorithms: Hill climbing search, simulated annealing, local beam search, genetic algorithms, searching in different environments, adversarial search
• Planning: Classical planning, planning as state-space search
• Knowledge Representation: Horn clause, resolution, theorem proving, ontology engineering, representing objects and events
• Natural Language Processing: Language models, text classification, information retrieval, information extraction

• In-course Assessments (Theory) —————————–15%
• In-course Assessments (Practical) —————————15%
• End-of-course Examination ———————————-70%

• S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Ed., Prentice Hall, 2010.
• G.F. Luger, Artificial Intelligence – Structures and Strategies for Complex Problem Solving, 6th Ed., Pearson & Addison Wesley, 2009.
• P. H. Winston, Artificial Intelligence, 1st Ed., Addison Wesley, 1993.

• Discuss basics of high performance computing and their usage
• Describe different parallel architectures, interconnection networks
• Transform sequential algorithms into efficient parallel algorithms
• Devise parallel programming models and parallel algorithms for solving computational problems
• Analyse parallel programming paradigms and their semantics and correctness issues
• Assess parallel algorithms based on their complexity and scalability

• Introduction to High Performance Computing: Cluster computing, grid computing, cloud computing, parallel & distributed computing, fault tolerance, concurrency, nondeterminism, locality
• Parallel Architectures: Taxonomy, data versus control parallelism (SIMD/Vector, pipelines, MIMD, multi-core, heterogeneous), shared versus distributed memory, interconnection networks for parallel computers
• Scheduling and Analytical Modeling: Cost of computation and scalability, model-based notions, handling scheduling issues
• Parallel Algorithms: Communication operations, algorithmic paradigms (Divide and conquer, recursion, Series, parallel composition), computation on matrices, sorting, graph algorithms, search algorithms
• Concepts of Parallel Programming: Distributed-memory programming (MPI), shared-memory programming (OpenMP, CUDA)

• In-course Assessment (Theory) —————————–15%
• In-course Assessment (Practical) —————————15%
• End-of-course Examination ———————————-70%

• J.C. Zbigniew, Introduction to Parallel Computing, 1st Ed., Cambridge University Press, 2017.
• J. Sanders, and E. Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st Ed., Addison-Wesley Professional, 2010.
• A. Grama, A. Gupta, G. Karypis, and V. Kumar. “Introduction to Parallel Computing”, 2nd Ed., Addison-Wesley, 2003.

• Describe the basic concepts of image processing and computer vision
• Perform visual tasks in sequences of image analysis operations, representations, specific algorithms, and inference principles
• Explain image processing techniques in the spatial and frequency domain
• Analyse a range of algorithms for image processing and computer vision
• Develop basic computer vision algorithms for image retrieval and image recognition
• Apply image processing and computer vision techniques to solve real-world problems

• Digital Image Fundamentals: Image representation, sampling and quantisation, image size, resolution, pixel neighbours, connectivity, arithmetic and logical operations for images
• Image Enhancement in Spatial Domain: Intensity transformations, histogram equalization and specification, filter operations for smoothing, sharpening images, and noise reduction
• Image Enhancement in Frequency Domain: The Fourier transform and its properties, Fast Fourier Transform (FFT), filter operations for smoothing, sharpening images, and noise reduction
• Morphological Image Processing: Dilation and erosion, opening and closing, basic morphological applications
• Image Segmentation: Thresholding, edge detection, region growing
• Introduction to Computer Vision and its Applications: Human eye-brain system as a model for computer vision, biometric applications, automated navigation
• Introduction to Object Recognition: Feature types and descriptors, template matching, bag-of-features framework, feature matching, convolutional neural networks (CNNs)

• In-course Assessment (Theory) —————————–15%
• In-course Assessment (Practical) —————————15%
• End-of-course Examination ———————————-70%

• W. Burger and M.J. Burge, Principles of Digital Image Processing: Fundamental Techniques, Springer, 3rd Ed., 2009.
• M. Sonka, R. Boyle and V. Hlavac, Image Processing, Analysis and Machine Vision, 3rd Ed., Springer, 2008.
• R.C. Gonzalez and R.E. Woods, Digital Image Processing, 3rd Ed., Pearson, 2007.
• L.G. Shapiro and G. Stockman, Computer Vision, Prentice Hall, 2001.

• Describe a range of supervised, unsupervised and reinforcement learning algorithms
• Explain different deep learning techniques
• Perform pre-processing operations on data to mine useful information
• Identify appropriate learning paradigms for given data mining problems
• Apply machine learning algorithms on data to identify new patterns or concepts

• Introduction to Machine Learning: Machine intelligence and applications, concepts, instances, attributes and their types, and handling sparse data, missing & inaccurate values in data, handling categorical data
• Supervised Learning: Introduction to classification and regression, rule-based learning, decision tree learning, Naive Bayes, k-nearest neighbour, support vector machines, neural networks, linear regressions, introduction to boosting
• Unsupervised Learning: K-means clustering, Gaussian mixture models (GMMs), hierarchical clustering
• Reinforcement Learning: Markov decision processes (MDP), value functions, returns and value functions, Bellman equation and optimality
• Introduction to Deep Learning: Convolutional neural network (CNN), Recurrent neural network (RNN)
• Dimensionality Reduction: PCA, feature selection
• Experimental Setup and Evaluation: Training and testing, cross-validation, confusion matrices and evaluation measures such as accuracies, mean square errors, ROC values

• In-course Assessment (Theory) —————————–15%
• In-course Assessment (Practical) —————————15%
• End-of-course Examination ———————————-70%

• C.M. Bishop, “Pattern Recognition and Machine Learning”, 2007.
• R.O. Duda, P.E. Hart, D.G. Stork, “Pattern Classification”, 2nd Ed., Wiley, 2000.
• T. Mitchell, “Machine Learning”, McGraw Hill, 1997.
• I.H. Witten, E. Frank, M.A. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, 3rd Ed., Morgan Kaufmann Series, 2011

• Describe the concepts of mobile wireless communications
• Discuss realistic problems in wireless communication
• Identify latest research trends in mobile computing
• Apply knowledge for mobile applications development
• Appraise routing and forwarding protocols for mobile ad hoc networks
• Recommend ad-hoc network based solutions for real world problems

• Overview of Wireless Networks: Wireless communication properties, wireless impairments, multiplexing in wireless communications, the need for a specialized MAC
• Routing in Wireless Networks: issues in routing for wireless networks, wireless routing protocols
• Ad-hoc Networks: mobile ad-hoc networks, sensor networks and vehicular ad-hoc networks, routing in ad-hoc networks, latest trends in ad-hoc networking
• Mobile Applications Development Environments: mobile platforms, development and deployment of applications
• Common Paradigms in Mobile Computing: low power computing, mobile computing in resource constrained environments, fault tolerance, and persistence

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• J. Schiller, “Mobile Communications”, 2nd Ed., Addison Wesley publishers, 2004.
• M. Yener and O. Dundar, “Expert Android Studio”, 1st Ed., Wrox publications, 2016.

• Apply advanced relational modeling techniques to design database
• Perform query optimisation and transaction processing
• Plan concurrency control and recovery mechanisms to improve data management
• Describe distributed database design and concepts

• Relational Modeling: Concepts of data modeling, Enhanced Entity-Relationship model, use of Unified Modeling Language (UML), higher level normalisation
• Physical organisation of databases: Storage and file structure, Indexing, Database efficiency and tuning
• Query optimisation: Factors governing query optimization, Centralized query optimization
• Transaction processing: Transactions, Concurrency control, Transaction management, Failure and recovery in centralised database
• Distributed Database Management Systems: Distributed database design, distributed transaction management, Concurrency control and recovery
• Advanced concepts and technologies in DBMS: Enhanced data models for advanced applications and Parallel DBMS architectures

• In-course Assessments —————————- 30%
• End-of-course Examination ———————- 70%

• Ramez Elmasri and Shamkant B. Navathe, Fundamentals of Database Systems, 7th edition, Addison-Wesley, 2015.
• J. Date, An Introduction to Database Systems, 8th edition, Addison-Wesley 2003.
• Ramakrishnan and Gehrke, Database Management Systems, 3rd edition, McGraw-Hill 2003.

• Appraise the principles underlying data communications
• Describe different layered architectures and protocols in computer networks
• Discuss routing and switching principles, algorithms and practical examples in computer networks
• Differentiate LAN standards, topologies, hardware and LAN selection criteria for enterprise usage
• Plan a network design about services and application by taking into account of quality of service, scalability and maintenance
• Examine data security and integrity techniques in networking perspectives
• Illustrate latest advancements in networking technologies

• Fundamentals of digital communications: Design issues related to data transfer, compare and contrast the circuit and packet switching technologies, multiplexing techniques, error control mechanisms
• Network architectures: Principles of layered architecture, roles of layers in the OSI and TCP/IP models
• Internet protocols: Application layer protocols in the Internet, TCP/IP protocol suite, Transport and network protocols with an emphasis on TCP/IP model, IP addressing and subnetting, troubleshooting in IP networks, IP routing mechanisms, IP versions 4 and 6, MAC layer and its protocols
• Routing and Switching: Routing and switching fundamentals, router architecture, routing algorithms, issues to consider in designing routing protocols
• Local area networks: LAN technologies such as IEEE 802 LAN standards, channel allocation in LAN segments and solutions, Ethernet and Ethernet networking using hubs and switches, problems associated in deploying wireless LANs and solutions
• Wide Area Networks: Technical issues related to the WAN, technologies available in establishing WAN infrastructure, WAN architectures, routing in WANs
• Network Security: Error detection and correction, encryption and decryption, viruses, worms, and hacking
• Latest advancements in computer networking: Software Defined Networks and Information Centric Networks

• In-course Assessments —————————- 30%
• End-of-course Examination ———————- 70%

• J. F. Kurose and K. W. Ross, Data Communication and Computer Networks: A top-down approach, 6th Ed. Pearson Education, 2013.
• A. S. Tanenbaum, and D. J. Wetherall, Computer Networks, 5th ed., Pearson Education, 2011.

• Perform visual tasks in sequences of image analysis operations, representations, specific algorithms, and inference principles
• Analyse a range of algorithms for image processing and computer vision
• Use point processing, filters and arithmetic operations in digital images
• Describe the roles of machine learning in computer vision
• Develop basic image processing and computer vision algorithms

• Basic image processing: Sampling and quantization, image size, resolution, and image file formats, image compression
• Image enhancement in the spatial domain: Intensity transformations of brightness, contrast, inverting images and thresholding operation, histogram processing: histogram equalisation and specification
• Filters and convolution: First order and second order operators for smoothing and sharpening
• Morphological image processing: Dilation and erosion, opening and closing, basic morphological applications
• Feature extraction: Shape features using template matching and Hough transform, multi-scale feature detection and matching: SIFT (scale invariant feature transform), SURF (speeded-up robust features)
• Frequency domain processing: The Fourier transform and its important properties, Fast Fourier Transform (FFT), processing images in frequency domain
• Understanding image data and performing classification and recognition: Introduction to biometric applications, approaches to object recognition, human eye-brain system as a model for computer vision

• In-course Assessments —————————- 30%
• End-of-course Examination ———————- 70%

• Burger, W., and Burge, M.J., Principles of Digital Image Processing: Fundamental Techniques, Springer, 2009.
• Sonka, Hlavac and Boyle, Image Processing, Analysis and Machine Vision, 3rd Ed., PWS Publishing, 2008.
• Gonzalez et al., Digital Image Processing, 3rd Ed., Pearson, 2008.
• Stockman and Shapiro, Computer Vision, Prentice Hall, 2001.

• Apply acquired knowledge in industrial environment
• Develop interpersonal, communication, management and team working skills
• Adapt to work readily in real industrial projects
• Perceive state-of-the-art industrial  technologies

• Students will be trained during the second semester of Level-3 in an appropriate ICT field in an industry for a period of Four to Six months which amounts to 800 notional hours under the guidance of academic and industrial supervisors.
• Any additional stay at the industry will not carry any additional credits.
• It is the student’s responsibility to find a placement in an industry for him/her in consultation with the Department. The department may assist the student in finding a placement in the industry.
• Student shall maintain a journal to record their progress activity during the training.
• Academic staff will visit the training institution at least once during the training period to monitor their progress in training.
• On completion of the industrial training, each student shall submit the report, journal and deliver an oral presentation.
• (*) Students who fail to obtain a minimum grade of C- and prefers to process to Level-4 will be required to do an additional project, considering the 40% marks given to the final project report as indicated in the evaluation criteria.
  • The project work will be assigned by an academic panel of the Department.
  • The individual project amounts to 300 notional hours that shall be continually monitored, assessed and evaluated by an academic panel of the Department.
  • On completion each student should submit a project report with demonstration.
  • The final grade of the students who complete their individual projects shall not exceed a grade of B.

• Training journal —————————————————————- 20%
• Evaluation of Mentor report from the industrial supervisor ————– 20%
• Presentation ——————————————————————–20%
• Final Report* —————————————————————— 40%

• Perform algorithm analysis using appropriate techniques
• Apply algorithms for solving problems of various complexity
• Identify suitable data structure(s) for efficient problem solving
• Formulate a real world problem into a model solvable by standard algorithmic approaches
• Defend NP-Hard & NP-Complete problems

• Analysis of algorithms: Order of growth and asymptotic notation, master method, amortised analysis
• Algorithms for sorting and order statistics: Standard sorting algorithms, sorting in linear time, medians, order statistics
• Advanced data structures: Hash table, binary heap, binary search tree, red-black tree, augmenting data structures, B-trees, Fibonacci heaps, utilising data structures for problem solving.
• Dynamic programming: Elements of dynamic programming (optimal substructure, overlapping sub-problems, memoization), application of dynamic programming approach for problem solving
• Greedy algorithms: Elements of Greedy strategy (optimal substructure, overlapping sub-problems, Greedy strategy), comparison of dynamic programming and Greedy approach, application of Greedy approach for problem solving
• P, NP, NP-Hard and NP-Completeness: Introduction to class of problems, NP-completeness (Cook’s theorem), classic NP-complete problems, reduction techniques

• In-course Assessments —————————————-30%
• End-of-course Examination ———————————-70%

• Tardos, J. K. E., Algorithm Design, 2nd Ed., Pearson Education, 2011.
• Sedgewick, R., Algorithms, 4th Ed., Addison-Wesley Professional, 2011.
• Thomas, H. Cormen, T. Leiserson, C. E., Rivest, R. L., and Stein, C., Introduction to Algorithms, 3rd Ed., MIT Press, 2009.

• Discuss the functionalities of a compiler
• Represent a specified language using nondeterministic and deterministic finite automata
• Analyse the syntax of a language using top-down and bottom-up parsing approaches
• Discuss implementation techniques for a compiler
• Design algorithmic routines for type checking and code generation

• Introduction: Compilers, Interpreters, and phases of compilation process
• Lexical Analysis: Regular expressions and their properties, converting regular expressions to Nondeterministic Finite Automata (NFA), Converting NFA to Deterministic finite automata (DFA), Eliminating dead states and minimising DFA
• Syntax Analysis: Context-free grammars, Syntax trees, Ambiguity in Grammar, Operator precedence, eliminating left-recursion, Left-factorisation, Top-down parsing methods, Bottom-up parsing methods, Resolving conflicts in parsing tables
• Scope and Symbol Tables: Dealing with scopes of identifiers, Efficient implementations of symbol tables
• Type Checking: The design space of types, Attributes, Type checking procedures
• Code Generation: Syntax Directed Translations, Intermediate-Code Generation, Machine-Code Generation

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Mogensen, T. E., Introduction to Compiler Design, 2nd Ed., Springer, 2017.
• Lam, M. S., Sethi, R., and Aho, A. V., Compilers: Principles, Techniques, and Tools, 2nd Ed., Pearson Education, 2013.
• Galles, D., Modern Compiler Design, 2nd Ed., Pearson Education, 2009.

• Discuss fundamental concepts of linear algebra in relation to data science
• Analyse data-driven problems using probability and statistics
• Apply mathematical optimization techniques for solving data-driven problems
• Build neural networks using backpropagation algorithm
• Transform data-driven problems into computer programs
• Analyse data through visualization

• Linear algebra: Overview of scalars, vectors, matrices, tensors, multiplication of matrices and vectors, norms, trace, rank, eigenvalues, and eigenvectors
• Probability and statistics: Overview of probability, marginal and conditional probabilities, independence and conditional independence, probability density functions, expectation, variance and covariance, and Bayes rule; Bayesian classifier, principal component analysis (PCA), linear discriminant analysis (LDA).
• Information theory: Entropy, cross-entropy, KL divergence, mutual information
• Multivariate calculus and mathematical optimization for parameter estimation: partial derivatives, chain rule, gradient based optimization (Gradient Descent, Stochastic Gradient Descent), Jacobian and Hessian matrices, constrained optimization, neural networks and back propagation algorithm.
• Applications: Eigen faces, LDA based classification, Linear Regression, Least square minimization
• Emerging Technologies in Big Data Analytics: Usage of Open source frameworks (e.g. Hadoop).

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Goodfellow, I., Bengio, Y. and Courville, A., Deep Learning, 1st Ed, MIT Press, 2016.
• Bishop, C.M., Pattern Recognition and Machine Learning, 1st Ed, Springer, 2006.
• Nielsen, M., Neural Networks and Deep Learning, Determination Press, 2019.
• Aggarwal, C. C., Linear Algebra and Optimization for Machine Learning, 1st Ed, Springer Nature, 2020

• Identify various security threats and attacks on Information Systems.
• Explain security design principles.
• Elaborate techniques for data protection on Information Systems.
• Understand the techniques used to protect Computer Networks
• Explain the protocols that are utilised to protect the network and application layers of the Internet.

• Introduction: Key security concepts, Critical characteristics of Information System, Secure-System life cycle, Security Professionals and the Organization.
• The need for security: Threats, Attacks, Secure software development.
• Security technologies: Firewalls, Virtual private networks, Intrusion detection and prevention systems, other security tools.
• Cryptography: Classical encryption techniques, Block ciphers, Data encryption standard, Advanced encryption standard, Public-Key cryptosystems.
• Cryptographic data integrity algorithms: Cryptographic hash functions, Message authentication codes, Digital signatures.
• Network security: Network access control and cloud security, Transport-level security, Wireless network security.
• Internet security: Internet Protocol security, web security (S-HTTP), email threats and email security (S/MIME)

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Whitman, M. E. and Mattord, H. J., “Principles of Information Security”, 6th Ed., Cengage Learning, 2017.
• Stallings, W., “Cryptography and Network Security: Principles and Practice”, 4th Ed, Pearson Education Limited, 2017
• Paar, C. and Pelzl, J., “Understanding Cryptography: A Textbook for Students and Practitioners” 1st Ed, Springer, 2014.

• Administer computer systems and connectivity devices
• Configure systems and devices for different networking scenarios
• Design a data communication network required for a small/medium organisation
• Demonstrate ability in network and server management
• Implement security policies in networked systems

• Host Management: Host hardware and maintenance, Basic commands, Files, Directories and File System, Editors, Processes, Users and group management, Package management, Automating system administration.
• Network Design and Management: Network connectivity devices, Host network configuration, Routing and Router configuration, IP addressing, subnetting, Switch configuration, Wireless equipment, VLAN, Inter-VLAN routing,
• Server Management: Install and manage server operating systems, Web server, e-mail server, Proxy server, DNS server, Content Servers,
• Security Policy Implementation: Firewall configuration, IP tables, Secure remote administration, Simple Intrusion Detection Techniques, Snort.

• In-course Assessments (Theory) —————————————20%
• Group Project ——————————————————————-20%
• End-of-course Examination (Practical) ——————————–60%

• Blokdyk, G., “Computer Network Administration: A Clear and Concise Reference”, 1st Ed, 5 Star Cooks, 2019
• Frisc, A., “Essential System Administration”, 3rd Ed, O’Reilly Media Inc., 2002.
• Odom, W., CCNA 200-301 Official Cert Guide Library, 1st Ed, Cisco Press, 2019
• Bauer, M. D., “Linux Server Security”, 2nd Edition, O’Reilly Media Inc., 2005

• Identify a hypothesis and/or a research problem
• Formulate the detailed problem statement
• Frame a solution with appropriate research methodology
• Validate the proposed solution
• Perform scientific communication
• Defend the performed research, results and findings

• Engage in an academic year long research project under the guidance of academic supervisor(s) with optional guidance by an external mentor
• Develop a research proposal with adequate literature review
• Carry out the research using appropriate research methodology
• Document and present the research

• Presentation of research proposal——————————————20%
• Proposed solution and Implementation—————————————40%
• Project Diary————————————————————–10%
• Project report————————————————————-10%
• Viva voce——————————————————————10%
• Submission of abstract/poster/paper to a scientific forum——————10%

• Apply acquired knowledge in industrial environment
• Develop interpersonal, communication, management and team working skills
• Adapt to work readily in real industrial projects
• Perceive state-of-the-art industrial technologies

• The industrial training is offered during the second semester.
• Students will be trained in an appropriate industry for a period of four to six months which amounts to 600 notional hours under the guidance of academic and industrial mentors.
• Any additional stay at the industry will not carry any additional credits.
• It is the student’s responsibility to find a placement in consultation with the department. The department may assist the student in finding a placement in an appropriate industry.
• Students shall maintain a journal to record their progress activity during the training.
• Academic staff will visit the training institution at least once during the training period to monitor their progress.
• On completion of the industrial training, each student shall submit the report, journal and deliver an oral presentation.
• Students who fail to obtain a minimum grade of D+ in industrial training may opt for a general degree in Computer Science as the training is non-repeatable.

• Training journal—————————————-20%
• Progress as per feedback from mentor(s)—————–20%
• Final Report——————————————–20%
• Presentation——————————————–40%

• Demonstrate the theoretical or logical rationale of a research problem
• Develop critical thinking by performing appropriate literature review
• Engage in a mentor-mentee relationship
• Practice scientific writing

• Introduction to research and identifying a study topic: Steps in the process of research, Ethics of research and informed consent
• Introduction to research proposal writing: Primary research question, testing this question, expected test results, implications of the research, timeline of the research
• Introduction to literature review and formulate research questions
• Research methodologies: Familiarizing both qualitative and quantitative methodologies, Experimental designs, Evaluation and problem analysis
• Citation formats, referencing styles and use of scientific literature digital libraries
• Use of ICT tools for checking plagiarism and LaTeX for scientific writing

•  Reading and presenting of research Papers ————- 20%
• Developing a sample research proposal —————— 50%
• Oral presentation of the research proposal ————– 30%

• Zobel, J., Writing for Computer Science, 2nd Ed., Springer, 2004.
• Materials will be provided in-class and via the course website

• Formulate an efficient Intelligent system model for a problem expressed in natural language
• Apply appropriate uninformed, informed or local search algorithms for solving problems
• Devise a plan of action to achieve a goal using standard AI methods
• Demonstrate the working of natural language processing techniques

• Introduction: Practical examples of Artificial Intelligence, Intelligent Agents, Environments, Intelligent behaviour, Rational behaviour & Turing test
• Problem solving by Searching: Problem-Solving Agents, Uninformed Search Strategies, Informed (Heuristic) Search Strategies
• Local search and optimization algorithms: Hill climbing search, Simulated annealing, Local beam search, Genetic algorithms, searching in different environments, adversarial search
• Planning: Classical planning, planning as state-space search
• Learning Methodologies: Learning by Analysing Difference, by Recording Cases, by Correcting Mistakes, by Building Multiple models, by Building Identification Tree
• Knowledge representation: Ontology engineering, Categories and objects, events
• Natural Language Processing: Language models, Text classification, Information retrieval, Information extraction

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Ed., Prentice Hall, 2010
• George F Luger, Artificial Intelligence – Structures and Strategies for Complex Problem Solving,
  6th Ed., Pearson & Addison Wesley, New York, London, Singapore, 2009
• Patrick H. Winston, Artificial Intelligence, 1st Ed., Addison Wesley, New York, England, Singapore, 1993

• Perform analysis of algorithms using appropriate techniques
• Identify most suitable data structures for efficient problem solving
• Transform problems in order to solve them using standard algorithmic approaches
• Apply algorithms for solving problems of various complexity
• Demonstrate understanding of NP-Hard & NP-Complete problems

• Analysis of algorithms: Order of growth and asymptotic notation, master method, amortised analysis
• Advanced data structures: Hash table, binary heap, binary search tree, red-black tree, augmenting data structures, B-trees, Fibonacci heaps
• Sorting and order statistics: Standard sorting algorithms, sorting in linear time, medians, order statistics
• Dynamic programming: Elements of dynamic programming – optimal substructure, overlapping sub-problems, memoization; application of dynamic programming approach for standard problems
• Greedy approach: Elements of Greedy strategy – optimal substructure, overlapping sub-problems, Greedy strategy; comparison of dynamic programming and Greedy approach, application of Greedy approach for standard problems
• P, NP, NP-Hard and NP-Completeness: Introduction to class of problems, NP-completeness (Cook’s theorem), classic NP-complete problems, reduction techniques

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein, Introduction to Algorithms, 3rd Ed., MIT Press, 2009.
• Jon Kleinberg Eva Tardos, Algorithm Design, 2nd Ed., Pearson Education, 2011.

• Discuss basics of high performance computing and their usage
• Describe different parallel architectures, interconnection networks, programming models, and parallel algorithms for common problems
• Assess parallel algorithms based on their complexity and scalability
• Analyse parallel programming paradigms and their semantics and correctness issues
• Transform problems in the computational area to efficient programming in parallel systems

• Introduction to high performance computing: Cluster computing, Grid computing, Cloud computing, Parallel & Distributed computing, Fault tolerance, Concurrency, Nondeterminism, Locality
• Parallel architectures: Taxonomy, Data versus Control parallelism (SIMD/Vector, Pipelines, MIMD, Multi-core, Heterogeneous), Shared versus Distributed memory, Interconnection networks for parallel computers
• Scheduling and Analytical modeling: Cost of computation and Scalability, Model-based notions, Handling scheduling issues
• Parallel Algorithms: Communication operations, Algorithmic paradigms (Divide and conquer, recursion, Series, parallel composition), Computation on matrices, Sorting, Graph algorithms, Search algorithms
• Concepts of Parallel Programming: Machine model, Control statements, SPMD notions including MPI and CUDA, Semantics and correctness issues

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Zbigniew J. Czech, Introduction to Parallel Computing, 1st Ed., Cambridge University Press, 2017
• Jason Sanders,‎ Edward Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st Ed., Addison-Wesley Professional, 2010
• Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar. “Introduction to Parallel Computing”, 2nd Ed., Addison-Wesley, 2003.

• Describe the fundamental concepts of mobile computing
• Explain realistic problems in wireless communication
• Identify latest research trends in mobile computing
• Demonstrate knowledge in mobile development platforms

• Overview of Wireless Networks: Wireless communication properties, wireless impairments, multiplexing in wireless communications, the need for a specialized MAC
• Routing in Wireless networks: issues in routing for wireless networks, wireless routing protocols
• Ad-hoc networks: mobile ad-hoc networks, sensor networks and vehicular ad-hoc networks, routing in ad-hoc networks, latest trends in ad-hoc networking
• Mobile applications development environments: mobile platforms, development and deployment of applications
• Common paradigms in mobile computing: low power computing, mobile computing in resource constrained environments, fault tolerance, and persistence.

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Jochen Schiller, “Mobile Communications”, 2nd Ed., Addison Wesley publishers, 2004.
• Murat Yener and Onur Dundar, “Expert Android Studio”, 1st Ed., Wrox publications, 2016.

• Identify a hypothesis and/or a research problem\
• Formulate the detailed problem statement
• Frame a solution with appropriate research methodology
• Validate the proposed solution
• Communicate details of performed research

• Engage in an academic year long research project under the guidance of academic supervisor(s) with optional guidance by a mentor from industry
• Develop a research proposal with adequate literature review
• Carry out the research using appropriate research methodology
• Document and Present the research

• Presentation of research proposal  ——————————————- 10%
• Implementation of proposed solution ————————————— 50%
• Project report ——————————————————————- 20%
• Oral presentation of project ————————————————— 10%
• Submission of abstract/poster/paper to a scientific forum ————— 10%

• Describe a range of supervised, unsupervised and reinforcement learning algorithms
• Perform pre-processing operations on data suitably to mine useful information
• Identify appropriate learning paradigms for given data mining problems
• Apply machine learning algorithms on data to identify new patterns or concepts

• Introduction to machine learning: Machine intelligence and applications, concepts, instances, attributes and their types, and handling sparse data, missing & inaccurate values in data
• Decision tree learning: Learning trees from training examples, Entropy and information gain, ID3 algorithm
• Supervised learning: Rule-based learning, Naive Bayes, k-Nearest neighbour, Neural networks
• Unsupervised learning: K-means clustering, Gaussian mixture models (GMMs), Hierarchical clustering
• Regression and Model-fitting Techniques: Linear regression, Polynomial Fitting, Kernel Based Networks
• Experimental setup and evaluation: Training and testing, Cross-validation, Confusion matrices and ROC graphs
• Dimensionality reduction: PCA, Feature selection, Feature extraction, Filter methods
• Use of Weka Tool in machine learning tasks

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Christopher M. Bishop, “Pattern Recognition and Machine Learning”, 2007.
• Richard O. Duda, Peter E. Hart, David G. Stork, “Pattern Classification”, 2^nd, Wiley, 2000.
• Mitchell,T., “Machine Learning”,  McGraw Hill, 1997.
• Ian H. Witten, Eibe Frank, Mark A. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, 3^rd Ed., Morgan Kaufmann Series, 2011.

• Explain the functionalities of a compiler
• Represent a specified language using nondeterministic and deterministic finite automata
• Analyze the syntax of a language using top-down and bottom-up parsing approaches
• Design algorithmic routines for type checking and code generation
• Discuss memory management in compilers

• Introduction: Compilers, Interpreters, and phases of compilation process
• Lexical Analysis: Regular expressions and their properties, converting regular expressions to Nondeterministic Finite Automata (NFA), Converting NFA to Deterministic finite automata (DFA), Eliminating dead states and minimising DFA
• Syntax Analysis: Context-free grammars, Syntax trees and ambiguity, Operator precedence, Eliminating left-recursion, Left-factorisation, Top-down parsing methods, bottom-up parsing methods, Conflicts in parsing tables
• Scopes and Symbol Tables: Dealing with scopes of identifiers, and efficient implementations of symbol tables
• Type Checking: The design space of types, Attributes, Type checking procedures
• Code Generation: Syntax Directed Translations, Intermediate-Code Generation, Machine-Code Generation
• Memory management: Runtime stack, heap, dope vectors, garbage collection

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Torben Egidius Mogensen, Introduction to Compiler Design, 2^nd, Springer, 2017.
• Monica S. Lam, R. Sethi A. V. Aho, Compilers: Principles, Techniques, and Tools, 2^nd Ed., Pearson Education Limited, 2013.

• Discuss the fundamental concepts in numerical linear algebra
• Apply the matrix factorization algorithms to solve system of linear equations
• Examine the convergence of iterative methods for solving system of linear equations
• Analyze the convergence of algorithms for solving Eigen value problems
• Discuss the fundamental concepts in differential equations and the numerical methods for solving differential equations
• Apply the numerical methods for various types of differential equations
• Implement basic programs in computational mathematics software

• Numerical Methods for solving system of linear equations: Linear algebra Review, Gaussian Elimination, LU factorization, Operations count, Error Analysis, Iterative algorithms
• Eigen Value Problems: Eigen values and Singular values, Hessenberg Form, Power Methods, QR Algorithm, Inverse iteration
• Numerical Methods for Differential Equations: Differential Equations Review, Initial Value problems, Euler Methods, Runge-Kutta methods, Finite Elements Methods.

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• N. Trefethen, D. Bau, Numerical Linear Algebra, SIAM, 1997.
• R. Dormand, Numerical Methods for Differential Equations: A computational Approach, Taylor and Francis, 1996.

• Describe the principles, methods and techniques of systems development
• Discuss principles, methods and techniques for systems development with persons without specialized knowledge in this area
• Describe how systems analysts interact with users, management, and other information systems professionals
• Propose a phased system development methodology to implement a systems development project for a realistic problem
• Use a phased system development methodology to implement a systems development project

• Systems Analysis Fundamentals: Core concepts of system analysis and design, traditional and modern approaches to system analysis, system development life cycle, roles of a systems analyst
• Information Requirements Analysis: Interactive and unobtrusive methods for information gathering, agile modeling and prototyping
• The Analysis Process: Data flow diagrams, analysing systems using data dictionaries, process specifications and structured decisions, designing effective input and output, designing databases
• Project management: Project initiation and attributes, determining feasibility, asserting software and hardware needs, project stakeholders, project management tools and techniques
• Quality Assurance and Implementation: Ensuring data quality, six sigmas, quality assurance through software engineering, implementing information system, software testing process, evaluation techniques

• In-course Assessments ——————- 30%
• End-of-course Examination ————- 70%

• Jeffrey A. Hoffer, Joey F. George, Joseph Valacich, Modern Systems Analysis and Design, 7th Ed.,
  Pearson, 2014.
• Kenneth E. Kendall and Julie E. Kendall, Systems Analysis and Design, 8th Ed., Prentice Hall, 2011.
• Kathy Schwalbe, Information Technology Project Management, 7th Ed., Course Technology, 2014.

• Design a data communication network required for a small/medium organisation
• Configure network switches to fulfil the needs of an organisation
• Set up a server environment consisting web, domain, mail and proxy servers in an organisation using best practices
• Demonstrate knowledge in securing and administering servers and computer networks

• Host administration: Basic commands, Files, Directories and File System, Editors, Processes, Users and group management, Package management, Automating system administration
• Network design: Network cabling, Network devices, designing LANs, Preparing a Bill of Materials (BOM)
• Network administration: Setup and Administer network devices, Host network configuration, Routing and Router configuration, Switch configuration, Wireless equipment, VLAN, Inter-VLAN routing
• Server administration: Install and configure Web server, Email server, Domain server and Proxy server
• Securing servers and networks: Securing networks and servers, monitoring network and servers, setting up backups, setting up firewalls, and disaster recovery

• In-course Assessments —————————————–30%
• End-of-course Practical examination ———————— 40%
• End-of-course Theory Examination ————————– 30%

• Adam Haeder, Stephen Addison Schneiter, Bruno Gomes Pessanha, James Stanger, “LPI Linux Certification in a Nutshell”, 3rd Ed., O’Reilly Media, 2010.
• Christine Bresnahan, Richard Blum, “LPIC-2: Linux Professional Institute Certification Study Guide: Exam 201 and Exam 202”, 2nd Ed., Sybex, 2016.

• To understand the general notion of complexity classes, P and NP, completeness and hardness, and the relationships between classes by reduction.
• To be able to develop, and reason about the correctness and performance of algorithms.

• Advanced data structures : B – Trees, Binomial heaps, Fibonacci Heaps, Data structures for disjoint sets. Hash tables: Direct address tables and Hash tables, Hash functions, Open addressing, Perfect hashing.
• Matrix operations : Properties of matrices, Matrix multiplication algorithms, solving systems of linear equations.
• Linear Programming : Standard and slack forms, Formulating problems as linear programs, Simplex algorithm, Duality, Initial basic feasible solution.
• Number-Theoretic Algorithms : Elementary number-theoretic notions, Modular arithmetic, Solving modular linear equation, The Chinese remainder theorem, Powers of an element, The RSA public-key crypto system.
• String Matching : The na?ve string-matching algorithms, The Rabin-Karp algorithm, String matching with finite automata, The Knuth-Morris-Pratt algorithm.
• Computational Geometry : Line segment properties, Determining intersection of a pair of segments, Finding the convex hull, finding the closest pair of points.
• NP-Completeness : Polynomial time, Polynomial-time verification, NP-Completeness and reducibility, NP-completeness proofs, NP-complete problems.
• Approximation Algorithms : The vertex cover problem, traveling-salesman problem, set-covering problem.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To study advanced topics of Artificial Intelligence.
• To understand the basics of expert systems, Natural Language Processing and acquire knowledge for developing such systems.

• Expert Systems : Expert system structure, design issues and sample applications, Design and development of the knowledge base and the inference engine, Expert system shells, Expert system developing tools.
• Natural Language Processing : Stages of natural language processing, Morphological analysis, Syntactic analysis, Semantic and Pragmatic analysis, Natural language processing applications, Common-sense and Understanding.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To be able to apply a variety of numerical methods to the problems of finding solutions to large system of linear equations.
• To be able to apply some numerical methods to find eigenvalues of large matrices.
• To introduce the ordinary and partial differential equations.
• To be able to derive solutions to a broad range of ordinary differential equations and to solve partial differential equations.

• Systems of Linear Equations, Eigen value computation.
• Ordinary Differential Equations, Initial value Problem.
• Partial Differential Equations, Finite Difference Method, Finite Elements Method.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To test and understand the concepts learned in other course units.
• To design, plan and complete a computer science related project individually.

• Project Report (70%)
• Presentation (30%)

• To study the fundamentals of diverse areas of parallel and high performance computing.
• To understand the design, development, implementation and analysis of parallel algorithms.
• To get practical experience in parallel computing environment simulated over a local network of computers using MPI.

• Parallel models and architectures : SIMD, MIMD, shared memory and interconnection models, Hypercube multicomputers, perfect shuffle, systolic arrays. Distributed Memory Parallel Computers and Message Passing in MPI
• Designing Parallel Algorithms: Methodical design, Partitioning, Communication, Agglomeration, Mapping.
• Quantitative Design : Defining Performance’s approaches to Performance Modelling, Developing models, Scalability analysis, Experimental studies, Evaluating implementations, Communication cost model, Input /Output.
• Parallel algorithms : Searching, merging, sorting, prefix sums, broadcasting, routing algorithms for numerical and optimisation problems, matrix problems (tri diagonal system, triangular and banded system), FFT, Graph algorithms.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To study a methodical approach for developing computer systems including systems planning, analysis, design, testing, implementation and software maintenance.
• To learn the strategies and techniques of systems analysis and design for producing logical methodologies for dealing with complexity in the development of information systems.
• To find the overview of the people involved in information systems development – their roles and responsibilities, with special emphasis on the systems analyst.

• Information systems and their characteristics.
• Process of systems development : Underlying principles, Phases of systems development, traditional systems development life cycle, Alternative systems development approaches (model-driven, rapid application development, packaged software).
• Project Management : Gantt charts and project reporting.
• Systems analysis : Fact finding techniques, Documenting user requirements with Use Cases. Feasibility Analysis.
• System Design : Object modeling using UML, Data modeling using entity relationship diagrams, Process modeling using data flow diagrams. System testing.
• Input Output Design : Input Design considerations & tools and User Interface Design.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To introduce basic concepts and techniques of Data Mining and Machine learning.
• To develop skills of using recent data mining and machine learning software for solving practical problems.

• Introduction: Data Mining and Machine Learning, Data collection and ware house, Data cleaning and preparation for Knowledge Discovery, Knowledge Representation .
• Classification : Basic methods, Decision Trees, Rule-based methods.
• Neural networks : Introduction to neural networks, Multilayer neural networks, Error Back-propagation algorithm, RBF networks.
• Stochastic methods: Simulated annealing, Genetic algorithms, Genetic programming.
• Bayesian Learning : Bayesian decision theory (Continuous features, Discrete features), Bayesian belief networks.
• Clustering: Unsupervised Learning, K-means clustering, Unsupervised Bayesian learning, Kohonen networks.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To understand wide range of topics in compiler design.

• Formal Languages : Language definition, grammers, finite state automata, regular expressions, lexical analysis.
• Syntax analysis : BNF, context-free grammars, recognisers, parse trees, top-down parsing, recursive descent, ambiguity, left recursion, backtracking, Warshall’s algorithm, context clashes, Bottom-up parsing, operator precedence grammars, constructing precedence matrices, producing abstract syntax trees and semantic actions, symbol and type tables, syntax directed translation.
• Storage Allocation : Run-time stack, heap, dope vectors, garbage collection
• Code Generation : Stack machines, assembly Language, P-Code, generating code for some typical constructs, machine code generation and optimisation.
• Error Diagnostics and recovery: Types of errors (lexical errors, syntax errors, type errors, runtime errors), detection of errors, recovery.
• Compiler construction tools : Yacc, Lex.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To understand the fundamental concepts underlying current developments in mobile communication systems and wireless computer networks.
• To study the current trends in mobile computing, particularly wireless LAN and blutooth.

• Introduction to mobile computing : Wireless Communications, Wireless Networks.
• Data link layer : Channel allocation.
• Network layer: Mobile IP, Micro-mobility solutions to the host mobility problem, Routing in mobile Ad Hoc networks.
• Transport layer : TCP in wireless environments.
• Application layer: Adaptation, Disconnected/Weak-connected Operations, Mobile Applications/Services, User Interface Issues, Mobile agents, Security.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)