COL333/COL671 - Artificial Intelligence - Autumn 2016
Tuesday, Thursday, Friday 11-11:50 pm in LH114

Instructor: Mausam
(mausam at cse dot iitd dot ac dot in)
Office hours: by appointment, SIT 402
TAs: (office hours by appointment)
Yashoteja Prabhu (csz138234 at
Dilpreet Kaur (csz158041 at
Saurabh Goyal (csy147550 at
Arindam Bhattacharya (csz168114 at
Kunal Dahiya (anz168048 at
Bhargav Reddy (cs5120301 at
Swarnadeep Saha (mcs152355 at
Akshay Gupta (cs5130275 at
Surag Nair (ee1130504 at
Shikhar Murty (ee1130462 at
Barun Patra (cs1130773 at

Course Contents

Introduction; philosophy of intelligent agents; uninformed search; heuristic search; local search; constraint satisfaction; logic and satisfiability; adversarial search; classical planning; 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 26 Aug 4 Introduction AIMA Chapter 1
Beyond Turing Test
Applications of AI
Benefits/Risks of AI
Aug 5 Aug 10 Uninformed search AIMA Chapter 3.1-3.4
Beam Search
Intuition of Search Algorithms
Search Algorithms Performance
Uniform Cost Search vs. Djikstra's
Aug 10 Aug 12 Informed search AIMA Chapter 3.5-3.7
Depth First Branch and Bound
A*/IDA* Example
Aug 16 Aug 20 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 20 Sep 5 Programming Assignment 1
Format Checker
Aug 23 Sep 6 Constraint Satisfaction AIMA Chapter 6 (skip 6.3.3)
Conversion to Binary CSP
Constraint Programming
Sep 6Sep 15 Logic and Satisfiability AIMA Chapter 7, 8.1-8.3
Advanced SAT Solvers (Section 2.3)
Sep 9 Sep 23 Programming Assignment 2
Sep 16Sep 22 Adversarial search AIMA Chapter 5
How Intelligent is Deep Blue?
Minimax Applet
Sep 23 Oct 5 Programming Assignment 3

Sep 27 Sep 28 Decision Theory AIMA Chapter 16.1-16.3, 16.6

Sep 28 Sep 30 Markov Decision Processes AIMA Chapter 17.1-17.3

Oct 4 Oct 6 Partially Observable Markov Decision Processes AIMA Chapter 17.4
POMDP Tutorial

Oct 12 Nov 2 Programming Assignment 4

Oct 13 Oct 13 Heuristic Search for MDPs MDPs Book Chapter 4.1-4.3

Oct 14 Oct 18 Bandits and Monte Carlo Tree Search
Monte Carlo Planning (Sections 3.1-3.3)
AIMA Chapter 21.4
UCT for Go
Monte Carlo Planning
Oct 20Oct 21 Reinforcement Learning AIMA Chapter 21.1-21.3
TD Learning for Backgammon
Nov 1 Nov 15 Programming Assignment 5

Nov 1 Nov 1 Intro to Probability AIMA Chapter 13
History of Bayes Theorem
Nov 3 Nov 10 Bayesian Networks Representation AIMA Chapter 14.1-14.4
Influence Flow in Bayes Nets
Nov 11 Nov 15 Bayesian Networks Inference and Learning AIMA Chapter 14.5, 20
Log Probabilities
Nov 18Nov 18 Wrap Up


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


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.