AI, Monsoon 2023

Course Calendar

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	3.5-3.6
8	Hands-on Activities	Course Project Discussions
9	Search in Complex Environments	Local Search, Search with Non-deterministic Actions, Online Search	4.1, 4.3-4.5
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	Reinforcement Learning	Learning from Rewards, Passive and Active RL, Policy Search	22
31	Project presentations
32	Epilogue	Summarisation, Quiz
33	Reflections and Reviews	Self Reflections
34	End semester Exam

Exercises

Chapter 1 exercises pdf
Chapter 2 exercises pdf
Chapter 3 exercises pdf
Chapter 4 exercises pdf
Chapter 6 exercises pdf
Chapter 7 exercises pdf
Chapter 12 exercises pdf
Chapter 13 exercises pdf
Chapter 14 exercises pdf
Chapter 16 exercises pdf [Chapter 15, Indian Edition]
Chapter 17 exercises pdf [Chapter 16, Indian Edition]

AIMA Libraries

AIMA Python libraries and notebooks can be downloaded here or here

Installation instructions if downloaded from the second repository: extract and navigate to the folder containing the file setup.py. Then run:

pip install -r requirements.txt

pip install .

The libraries can be used as: from aima3.logic import * etc.