COL333/COL671 - Artificial Intelligence - Autumn 2019
Tuesday, Thursday, Friday 11-11:50 pm in LH 111

Instructor: Mausam
(mausam at cse dot iitd dot ac dot in)
Office hours: by appointment, SIT 402
TAs: (office hours by appointment)

Course Contents

Introduction; philosophy of intelligent agents; uninformed search; heuristic search; local search; constraint satisfaction; logic and satisfiability; adversarial search; decision theory; Markov decision processes; Bayesian networks representation, inference and learning; reinforcement learning; basics of supervised, semi-supervised and unsupervised learning; intro to deep learning; intro to NLP; current research trends.


Start End Topics & Lecture Notes Required Readings Additional Resources
Jul 23 Aug 1 Introduction AIMA Chapter 1
Beyond Turing Test
Applications of AI
Benefits/Risks of AI
Introduction to AI: Past, Present & Future
Aug 1 Aug 6 Uninformed search AIMA Chapter 3.1-3.4
Beam Search
Intuition of Search Algorithms
Search Algorithms Performance
Uniform Cost Search vs. Djikstra's
Aug 7 Aug 8 Informed search AIMA Chapter 3.5-3.7
Depth First Branch and Bound
A*/IDA* Example
Aug 9 Aug 16 Local search AIMA Chapter 4.1
Stochastic Beam Search
Evolving Monalisa through Genetic Algorithms
Evolving TSP with Genetic Algorithms
Mixability for Genetic Algorithms (pages 66-68)
Aug 13 Aug 29 Programming Assignment 1
Aug 20Aug 29 Adversarial search AIMA Chapter 5
How Intelligent is Deep Blue?
Minimax Applet
Aug 30 Sep 4 Constraint Satisfaction AIMA Chapter 6 (skip 6.3.3)
Conversion to Binary CSP
Constraint Programming
Aug 30 Sep 16 Programming Assignment 2
Sep 4Sep 6 Logic and Satisfiability AIMA Chapter 7, 8.1-8.3
Sep 6 Sep 13 Phase Transitions and Backdoors Advanced SAT Solvers (Sections 2.3, 2.4)
Phase Transitions
Sep 17 Sep 17 Intro to Probability AIMA Chapter 13
History of Bayes Theorem
Sep 19 Oct 10 Programming Assignment 3
Sep 19 Sep 24 Bayesian Networks Representation AIMA Chapter 14.1-14.4
Influence Flow in Bayes Nets
Oct 1 Oct 1 Approximate Inference in Bayesian Networks AIMA Chapter 14.5
Log Probabilities
Oct 1 Oct 20 Programming Assignment 4

Oct 10 Oct 10 Learning in Bayesian Networks AIMA Chapter 20
Oct 11 Oct 11 Agent Architectures AIMA Chapter 2

Oct 15 Oct 17 Decision Theory AIMA Chapter 16.1-16.3, 16.6

Oct 17 Oct 25 Markov Decision Processes AIMA Chapter 17.1-17.3

Oct 25 Nov 10 Programming Assignment 5

Oct 29Nov 1 Reinforcement Learning AIMA Chapter 21.1-21.3
TD Learning for Backgammon
Nov 5Nov 8 Introduction to Deep Learning and CNNs DL 6, 9.1-9.3

Nov 13Nov 13 Deep Reinforcement Learning Deep Q Networks

Nov 13Nov 14 Ethics of AI

Nov 14Nov 14 Wrap Up


Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach,
Prentice-Hall, Third Edition (2009) (required).

Ina GoodFellow, Yoshua Bengio & Aaron Courville, Deep Learning,
MIT Press (2016).


Assignments: 50%; Minor (each): 10%; Final: 30%; Class participation, online discussions: extra credit.

There will be five programming assignments due approximately every two weeks.

Course Administration and Policies

Cheating Vs. Collaborating Guidelines

As adapted from Dan Weld's guidelines.

Collaboration is a very good thing. On the other hand, cheating is considered a very serious offense. Please don't do it! Concern about cheating creates an unpleasant environment for everyone. If you cheat, you get a zero in the assignment, and additionally you risk losing your position as a student in the department and the institute. The department's policy on cheating is to report any cases to the disciplinary committee. What follows afterwards is not fun.

So how do you draw the line between collaboration and cheating? Here's a reasonable set of ground rules. Failure to understand and follow these rules will constitute cheating, and will be dealt with as per institute guidelines.