AI & Machine LearningSemester 2-1 syllabus

The first semester of second year builds the mathematical and programming backbone the AI&ML specialization rests on, introducing Artificial Intelligence as a standalone course alongside advanced data structures and Java. Two hands-on labs pair with the theory courses, and Python is introduced as a skill-enhancement course to prepare students for the data-centric work ahead. A humanities course on human values and an audit course on environmental science round out the semester’s non-technical requirements.

Subjects

Discrete Mathematics & Graph Theory

• Unit 1: Mathematical logic — statements, connectives, well-formed formulas, truth tables, tautologies, normal forms, and inference techniques for both propositional and predicate calculus.
• Unit 2: Set theory — set operations, inclusion-exclusion, relations (partitions, closures, partial orders, Hasse diagrams), and functions including bijections, composition, and recursion.
• Unit 3: Combinatorics and recurrence relations — counting principles, permutations and combinations, binomial/multinomial theorems, generating functions, and methods for solving recurrences.
• Unit 4: Graph theory fundamentals — subgraphs, adjacency/incidence representations, isomorphism, and Eulerian/Hamiltonian paths.
• Unit 5: Multigraphs — bipartite and planar graphs, graph colouring and covering, chromatic number, spanning trees, and Prim’s/Kruskal’s/BFS/DFS-based tree construction.

L-T-P: 3-0-0, 3 credits

Universal Human Values — Understanding Harmony and Ethical Human Conduct

• Unit 1: Introduction to value education — self-exploration as a process, and the distinction between values and skills for sustained happiness.
• Unit 2: Harmony within the individual — the self as distinct from the body, and self-regulation as a path to inner balance.
• Unit 3: Harmony in family and society — trust and respect as foundational relational values, extending to a vision of a harmonious social order.
• Unit 4: Harmony with nature — interconnectedness among the orders of nature and existence understood as co-existence.
• Unit 5: Professional ethics — translating holistic understanding into ethical conduct, humanistic education, and value-based professional practice.

L-T-P: 2-1-0, 3 credits

Artificial Intelligence

• Unit 1: Foundations — the history of AI, intelligent agents, rationality, environment types, and problem formulation.
• Unit 2: Search — uninformed strategies (BFS, DFS), heuristic search (hill climbing, A, AO), and adversarial game-playing including minimax and alpha-beta pruning.
• Unit 3: Knowledge representation — predicate logic, semantic networks, frames, rule-based systems, and reasoning under uncertainty using Bayesian and Dempster-Shafer approaches.
• Unit 4: First-order logic and learning — inference techniques, forward/backward chaining, resolution, and learning paradigms including decision trees and reinforcement learning.
• Unit 5: Expert systems — architecture, knowledge acquisition, heuristics, and classic systems such as MYCIN and DART.

L-T-P: 3-0-0, 3 credits

Advanced Data Structures & Algorithm Analysis

• Unit 1: Algorithm analysis fundamentals, asymptotic notation, and self-balancing/multi-way search structures — AVL trees and B-trees.
• Unit 2: Heap-based priority queues, graph representations and traversal, and divide-and-conquer techniques such as quicksort, mergesort, and Strassen’s algorithm.
• Unit 3: Greedy strategies (job sequencing, minimum spanning trees, shortest paths) and dynamic programming (all-pairs shortest paths, optimal BSTs, knapsack, TSP).
• Unit 4: Backtracking (N-Queens, subset sum, graph colouring) and branch-and-bound approaches to knapsack and TSP.
• Unit 5: Computational complexity theory — NP-hard and NP-complete problems, Cook’s theorem, and classic hard graph/scheduling problems.

L-T-P: 3-0-0, 3 credits

Object Oriented Programming through Java

• Unit 1: OOP fundamentals, Java program structure, data types, operators, and control-flow statements.
• Unit 2: Classes, objects, constructors, access control, and method design including overloading and recursion.
• Unit 3: Arrays and inheritance mechanics, plus interfaces including default and static methods.
• Unit 4: Packages, the Java class library, exception handling, and Java I/O.
• Unit 5: String handling, multithreading, JDBC-based database connectivity, and building simple JavaFX GUIs.

L-T-P: 3-0-0, 3 credits

Advanced Data Structures & Algorithm Analysis Lab

• Hands-on construction and manipulation of AVL trees, B-trees, and heaps.
• Graph traversal implementations (BFS/DFS) and detection of connected/biconnected components.
• Comparative implementation of sorting algorithms and shortest-path techniques.
• Applying greedy and dynamic-programming strategies to knapsack, job sequencing, and spanning-tree problems.
• Backtracking and branch-and-bound implementations for N-Queens and the travelling salesperson problem.

L-T-P: 0-0-3, 1.5 credits

Object Oriented Programming through Java Lab

• Core language exercises covering primitive types, control structures, and basic I/O.
• Class design exercises: constructors, overloading, and access modifiers.
• Inheritance, interfaces, and runtime polymorphism implementations, including custom exception classes.
• Multithreading exercises (thread creation, synchronization, producer-consumer) and package creation.
• JavaFX GUI building and JDBC-based database connectivity exercises.

L-T-P: 0-0-3, 1.5 credits

Python Programming

(Skill Enhancement Course)

• Unit 1: Python fundamentals — installation and tooling, core language elements, and control-flow statements.
• Unit 2: Functions, string handling, and list operations.
• Unit 3: Dictionaries, tuples, and sets, along with their built-in operations.
• Unit 4: File handling and an introduction to object-oriented programming in Python.
• Unit 5: A first look at data-science tooling — JSON/XML handling, NumPy arrays, and pandas data frames.

L-T-P: 0-1-2, 2 credits

Environmental Science

(Audit Course)

• Unit 1: The multidisciplinary nature of environmental studies and the state of renewable/non-renewable natural resources.
• Unit 2: Ecosystem structure and function, energy flow, and biodiversity conservation.
• Unit 3: Causes, effects, and control measures for major categories of pollution, plus solid-waste and disaster management.
• Unit 4: Social dimensions of environmental sustainability, environmental legislation, and climate-related issues.
• Unit 5: Population growth, human health, and welfare programmes, supported by field-based observation exercises.

L-T-P: 2-0-0 (non-credit audit course)

Semester total: 16-2-8 contact hours, 20 credits, plus a mandatory 8-week community service project internship during the following summer vacation.