Session No. |
Topic Title |
Topic and subtopic details |
Readings |
1 |
Introduction |
What is AI, Foundations of AI, Current state of AI |
1 |
2,3 |
Intelligent Agents |
Agents and Environment, Rationality, Nature of Environment, Structure of Agent |
2 |
4,5 |
Search Algorithms |
Problem solving agents, Best-first Search, Uninformed Search: Breadth First Search, Uniform cost search, Depth First Search |
3.1-3.4 |
6,7 |
Search Algorithms |
Informed Search: Greedy Best First search, A* search, Heuristic Functions, Local Search, AND-OR Trees |
3.5-3.6 |
8,9 |
Hands-on Activities |
Building Foundations for the Course Project |
|
10 |
Adversarial Search |
Game Theory, Game Trees, Stochastic Games |
5.1-5.5 |
11 |
Constraint Satisfaction Problems |
CSP, Inference in CSPs, Backtracking and local search for CSPs |
6.1-6.5 |
12 |
Logical Agents |
Logic, Propositional Logic, Propositional Theorem Proving, Propositional Model Checking, Propositional Logic Agents |
7.1-7.7 |
13 |
First Order Logic |
Introdution, Syntax and Semantics, Using FOL, Knowledge Engineering |
8.1-8.4 |
14 |
Inference in FOL |
Propositional vs. FOL Inference, Unification and FOL, Forward Chaining, Backward Chaining |
9.1-9.4 |
15 |
Mid-Semester Exam |
Topics covered till date |
|
16 |
Probability |
Representing knowledge in uncertain domain, Semantics of Bayesian Network, conditional independence relations, case study |
12 |
17 |
Automated Planning |
Algorithms and Heuristics for Classical Planning, Hierarchical Planning, Planning in Non-Determinstics Domains, Scheduling |
11.1-11.6 |
18, 19 |
Probabilistic Reasoning |
Bayesian Networks, Representation, Bayesian Networks Inference |
13.1-13.5 |
20 |
Probabilistic Reasoning over time I |
Time and Uncertainty, inference in temporal models, Hidden Markov Model |
14.1-14.3 |
21 |
Probabilistic Reasoning over time II |
Kalman Filters, Dynamic Bayesian Networks |
14.4-14.5 |
22 |
Making Simple Decisions |
Utility Theory, Utility Functions, Decision Networks, Value of Perfect Information |
16.1-16.6 |
23 |
Making complex decisions I |
Markov Decision Process |
17.1 |
24 |
Making complex decisions II |
Algorithms for MDPs, Bandit Problems |
17.2-17.3 |
25 |
Multiagent decision making I |
Multiagent Environments, Non-cooperative Game Theory |
18.1-18.2 |
26 |
Multiagent decision making II |
Cooperative Game Theory |
18.3-18.4 |
27 |
Machine Learning I |
Learning from examples, Model Selection, Theory of Learning, Linear Regression |
19 |
28 |
Machine Learning II |
Deep Learning |
21 |
29, 30, 31 |
Reinforcement Learning |
Learning from Rewards, Passive and Active RL, Policy Search |
22 |
32 |
Epilogue |
Summarisation |
|
33 |
Reflections and Reviews |
Self Reflections |
|
34 |
End semester Exam |
|
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