M.Sc. Computer Science (Artificial Intelligence)

The MSc Computer Science (Artificial Intelligence) programme is designed to equip students with advanced knowledge in artificial intelligence (AI), machine learning, and data science. The programme integrates theoretical foundations with hands-on experience in AI technologies, preparing students for careers in research, development, and industry applications.

Artificial Intelligence is revolutionizing industries, enabling automation, and enhancing decision making capabilities. This programme covers core concepts of AI, including machine learning, deep learning, natural language processing and data analytics. Students will gain expertise in designing intelligent systems that can analyze, predict, and solve real-world problems.

Core courses are the mandatory, foundational subjects that every student must complete to graduate. They build the advanced theoretical and technical baseline required for the degree, preparing students for the tech industry or further research.

This course builds a bridge between hardware architecture and system software. Module 1 explores Von Neumann architecture, RISC/CISC instruction sets, and ALU/Control Unit design. Module 2 covers OS functions, process concepts, and synchronization using semaphores/monitors. Module 3 details I/O hardware, DMA, and file system structures. Module 4 examines memory hierarchy, cache organization, and virtual memory paging. Module 5 focuses on CPU scheduling algorithms, deadlock handling (prevention/avoidance), and pipeline architecture for instruction execution. Students learn the foundational mechanics that govern how computers process data and manage system resources

This course provides a deep dive into OOP principles using Java. Module 1 covers object-oriented basics (encapsulation, abstraction, inheritance) and Java environment setup. Module 2 delves into control flow, class definitions, and method/constructor overloading. Module 3 addresses arrays, packages, interfaces, and exception handling strategies. Module 4 introduces multithreading, thread synchronization, and basic networking concepts (sockets, URLs). Module 5 focuses on GUI development using AWT and Swing, teaching event handling and frame creation. This course equips students with the skills to develop robust, platform-independent applications.

This course bridges the gap between raw data and algorithmic efficiency. Module 1 introduces ADTs and time/space complexity notation (Big-Oh). Module 2 covers stacks and queues, including expression evaluation via Polish notation. Module 3 details linked lists and recursion concepts (Towers of Hanoi). Module 4 explores tree structures (BST, AVL, B-trees) and graph algorithms (BFS, DFS, Dijkstra’s). Module 5 focuses on sorting, searching, and advanced pattern matching (Knuth-Morris-Pratt). Students master the selection of data structures that optimize program performance for specific computational tasks.

This course offers the mathematical rigor required for AI research. Module 1 provides a foundation in linear algebra (vector spaces, basis, rank). Module 2 covers analytic geometry, norms, and orthogonality. Module 3 details matrix decompositions, focusing on Eigenvalues, SVD, and diagonalization. Module 4 delves into vector calculus, covering partial derivatives, gradients, and Taylor series. Module 5 explores continuous optimization, specifically gradient descent, Lagrange multipliers, and convex optimization. These concepts are essential for understanding the theoretical underpinnings of machine learning and modern AI models.

This course equips students with statistical and probabilistic tools for data. Module 1 introduces fundamental descriptive statistics like mean, variance, and correlation. Module 2 covers probability theory, including Bayes' Theorem and distributions (Binomial/Normal). Module 3 explores sampling theory, point estimation, and confidence intervals. Module 4 focuses on hypothesis testing, covering Z-tests, Chi-square tests, and sequential analysis. Module 5 introduces probabilistic inference, including Bayesian reasoning, Gaussian mixtures, Gibbs sampling, and Logistic Regression. Students gain the ability to model uncertainty and perform rigorous statistical tests on real-world datasets.

This course provides a comprehensive introduction to Python programming. Module 1 covers basic logic, flowcharts, and Python syntax, including variables and operators. Module 2 details data types, such as strings, lists, tuples, sets, and dictionaries. Module 3 examines control flow, including conditional statements and loops. Module 4 focuses on modular programming, functions, recursion, and the use of built-in/custom modules. Module 5 explores file management, directory operations, and package handling. This course prepares students for data science tasks by mastering Python's versatile environment and rich library support.

This course explores how AI systems represent and reason with knowledge. Module 1 introduces intelligent agents and environments. Module 2 covers search strategies, including heuristic search, adversarial search, and game theory. Module 3 examines first-order logic and probabilistic reasoning for knowledge representation. Module 4 addresses planning and decision-making under uncertainty, including multi-agent environments. Module 5 delves into advanced representation using ontologies, graph databases (Allegrograph/Trinity), and distributed storage for sparse datasets. Students learn to build systems that mimic human reasoning and knowledge management.

This course provides an in-depth study of RDBMS and beyond. Module 1 covers DBMS architecture and data abstraction. Module 2 focuses on data modeling, ER diagrams, and normalization principles. Module 3 details SQL proficiency, including joins, sub-queries, stored procedures, and triggers. Module 4 examines distributed databases, covering fragmentation, replication, and concurrency control. Module 5 explores emerging trends, including object-oriented databases, NoSQL, and information retrieval systems. Students gain expertise in designing scalable systems that maintain data integrity while supporting complex analytical queries.

This course introduces the logic behind machine learning. Module 1 covers learning paradigms (supervised/unsupervised) and estimation methods. Module 2 focuses on dimensionality reduction (PCA) and classification models, including SVM and Neural Networks. Module 3 examines data clustering algorithms and regression techniques. Module 4 details Bayesian networks, HMM, and Viterbi algorithms. Module 5 focuses on Deep Learning components, including CNNs, RNNs (LSTM), and an introduction to the TensorFlow ecosystem. This course prepares students to build, train, and deploy predictive models for various applications.

This course covers the communication foundations of modern systems. Module 1 examines the network edge, protocols, and content distribution. Module 2 details transport layer protocols (TCP/UDP), network layer IP addressing (IPv4/IPv6), and routing. Module 3 focuses on data link layer services, including error detection, switching, and MAC protocols. Module 4 explores multimedia networking, focusing on streaming, RTP/RTCP, and audio/video compression. Module 5 covers network security, cryptography, message integrity, and network management systems. This provides a clear understanding of how distributed AI systems communicate and stay secure.

This course transforms data into knowledge. Module 1 covers data warehousing, OLAP, and data preparation. Module 2 explores classification algorithms, including Naïve Bayes and Decision Trees, alongside text processing methods like TFIDF. Module 3 details clustering (K-Means, DBSCAN) and frequent itemset mining (Apriori, FP-Trees). Module 4 addresses locality-sensitive hashing and topic modeling (LSI). Module 5 focuses on web mining, search engine ranking (PageRank), and recommendation systems. Students learn to handle vast datasets, making them actionable for business strategy.

Practical courses bridge the gap between theoretical algorithms and real-world software development wherein actively write and debug code, model databases, build AI systems, and complete hands-on projects.

Practical implementation of Java programming concepts, including multithreading, event handling, and socket programming.

Practical application of data structures and algorithms using C++, covering searching, sorting, hashing, and graph implementation.

Hands-on experience with machine learning algorithms, including classification, regression, clustering, and neural networks.

Practical application of database concepts using MySQL, including SQL commands, PL/SQL, and a micro-project.

Practical data analysis using R, alongside implementation of Big Data tools like Hadoop

A project and seminar component designed to train students in self-study, research, and technical presentation.

Elective courses are optional, specialized subjects that students choose alongside mandatory core classes to tailor the degree to specific career goals and interests.

This course applies graph theory to real-world network problems. Module 1 defines graphs, subgraphs, isomorphism, trees, and the shortest path problem. Module 2 addresses connectivity, Euler/Hamiltonian cycles, and matching problems. Module 3 covers independent sets, Ramsey's theorem, and vertex coloring/chromatic polynomials. Module 4 examines planar graphs, including Euler's formula and planarity algorithms. Module 5 focuses on directed graphs, flow networks, and the Max-Flow Min-Cut theorem for network optimization. Students learn to model complex systems—ranging from communication to road networks—using graph-theoretic tools to identify critical structures and optimize flow efficiency.

This course explores the theoretical foundations and practical applications of visual data processing. Module 1 covers image formation, sampling, quantization, and Fourier transforms. Module 2 focuses on digital morphology, feature detection, and segmentation techniques like K-means clustering. Module 3 details image restoration methods, classification (SVM), and ensemble learning (bagging/boosting). Module 4 addresses face recognition, object detection, and scene understanding. Module 5 examines state-of-the-art computational photography and medical image processing applications. Students learn to design systems that extract actionable intelligence from images and videos, bridging the gap between raw pixel data and high-level scene comprehension.

This course focuses on managing and analyzing massive, distributed datasets. Module 1 introduces Big Data paradigms and the Map-Reduce framework. Module 2 details massive data computing using commodity clusters and distributed algorithms for text processing. Module 3 covers NoSQL stores, including the Google File System, HBase, and Cassandra. Module 4 explores data stream mining, including sampling and filtering distinct elements in streaming data. Module 5 examines link analysis (PageRank, Hubs/Authorities) and recommendation engines, preparing students to extract insights from web-scale data while leveraging distributed infrastructure for efficient processing.

This course addresses the extraction of meaning from unstructured text. Module 1 covers basic NLP, tokenization, stemming, and machine translation. Module 2 details language modeling using N-grams, smoothing techniques (Good-Turing), and perplexity. Module 3 explores text classification and sentiment analysis using Naive Bayes and Maximum Entropy classifiers. Module 4 examines sequence labeling for Named Entity Recognition (NER), POS tagging, and syntactic parsing (CKY). Module 5 focuses on Information Retrieval (IR) systems, including inverted indices, ranked retrieval (TF-IDF), word sense disambiguation, and the semantics of word relations.

This course studies techniques for recognizing data patterns. Module 1 covers Bayesian decision theory, feature extraction, and PCA. Module 2 details non-parametric techniques, Parzen windows, and perceptron algorithms. Module 3 examines unsupervised learning (hierarchical/partitional clustering) and multidimensional scaling. Module 4 introduces fuzzy logic systems for pattern recognition and clustering (fuzzy c-Means). Module 5 addresses pattern recognition in intrusion detection, applying neural and statistical approaches to data security tasks. This course builds the skills to categorize data features into recognizable, actionable groups for classification tasks.

This course explores biologically inspired computing. Module 1 introduces soft computing, neural networks, and fuzzy logic. Module 2 details neural network models and their control system applications. Module 3 focuses on fuzzy set theory, reasoning, and clustering. Module 4 examines neuro-fuzzy modeling and genetic algorithms (crossover, mutation), alongside Ant Colony Optimization. Module 5 covers integer programming, including branch-and-bound methods and zero-one programming. Students learn to handle uncertainty and non-linear optimization in scenarios where traditional, exact mathematical models may be insufficient.

The final semester acts as the capstone. Students engage in an individual Major Project or Internship (minimum 450 hours), applying their knowledge of AI to real-world problems under industry or academic supervision. The project lifecycle covers literature survey, design, implementation, and finding/findings documentation. The Comprehensive Viva evaluates the student’s cumulative knowledge across all courses taken during the four semesters. This phase ensures that graduates are job-ready, having demonstrated the ability to bridge academic theory with professional practice.

Graduates of the MSc AI programme have excellent career prospects in both industry and academia. With the growing demand for AI professionals across multiple sectors, students can explore opportunities in:

  • Software Industry: AI Engineer, Machine Learning Engineer, Data Scientist, AI Research Scientist, Software Developer
  • Finance & Banking: AI-driven risk assessment, fraud detection, predictive analytics
  • Healthcare: Medical image analysis, AI-powered diagnostics, healthcare automation
  • Retail & E-commerce: Recommendation systems, demand forecasting, customer sentiment analysis
  • Robotics & Automation: Intelligent robots, autonomous vehicles, industrial automation
  • Academia & Research: PhD opportunities, AI research positions in top institutes
  • Government & Defense: Cybersecurity, surveillance, AI policy development