AD203 • Autumn 2026
Build strong foundations in Artificial Intelligence through intelligent agents, search, reasoning, reinforcement learning, deep learning, and modern Large Language Models.
Modules
Teaching Weeks
Programming Assignments
Credits
Course Objectives
Course Overview
Everything students need to know before starting the course.
Assistant Professor, Department of Computer Science
Teaching Assistant
Lecture • Tutorial • Practical
IIITDM Kurnool
Autumn 2026
Office Hours: TBA
Learning Journey
This course takes you from the fundamentals of Artificial Intelligence to modern Large Language Models through a carefully designed sequence of topics.
Understand intelligent agents, PEAS, rationality and problem formulation.
Learn BFS, DFS, A*, heuristic search, local search and optimization.
Master Minimax, Alpha-Beta pruning and Constraint Satisfaction Problems.
Bayesian Networks, inference, uncertainty and decision making.
Markov Decision Processes, Q-Learning, SARSA and Policy Iteration.
Deep Learning, Transformers, LLMs, Responsible AI and AI Safety.
Course Syllabus
Explore each module to see the topics covered throughout the semester.
Weekly Teaching Plan
Weekly lecture plan based on the official course syllabus.
Week 1
History of AI, Philosophy of AI, Definitions and Applications
Week 2
Intelligent Agents, PEAS, Environment Types, State Space Representation
Week 3
BFS, DFS, DLS, IDS, Uniform Cost Search
Week 4
Greedy Search, A*, Heuristics
Week 5
Hill Climbing, Simulated Annealing, Genetic Algorithms
Week 6
Games, Minimax, Alpha-Beta Pruning
Week 7
CSP, Backtracking, Arc Consistency
Week 8
Probability Review, Bayes Theorem
Week 9
Conditional Independence, Inference, Sampling
Week 10
Utility, Decision Networks
Week 11
Bellman Equations, Policy Evaluation, Value Iteration
Week 12
Monte Carlo, TD Learning, Q-Learning, SARSA
Week 13
Perceptron, MLP, CNN, RNN, DQN, Actor-Critic
Week 14
Transformers, Large Language Models, Responsible AI, Revision
Instructor
Assistant Professor
Grading Policy