CSL333/CSL671 - Artificial Intelligence - Spring 2015
Monday, Wednesday 11-11:50 am, Thursday 12-12:50 pm in IVLT3

Instructor: Mausam
(mausam at cse dot iitd dot ac dot in)
Office hours: by appointment, SIT 402
TAs: (office hours by appointment)
Sangeetha Krishnan (mcs138410 at cse.iitd.ac.in)
Abhishek Yadav (mcs132540 at cse.iitd.ac.in)
Kaustubh Kulkarni (mcs132562 at cse.iitd.ac.in)
Divyanshu Shekher (mcs132554 at cse.iitd.ac.in)
Dipanjan Chakraborty (dipanjan at cse.iitd.ernet.in)
Ankit Anand (ankit.s.anand at gmail.com)
Prachi Jain (p6.jain at gmail.com)
Himanshu Jain (csz138519 at cse.iitd.ac.in)
Gagan Bansal (gaganbansal1992 at gmail.com)
Aditya Grover (aditya.grover1 at gmail.com)
Kunal Chawla (cs1110225 at cse.iitd.ac.in)

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 NLP; intro to information retrieval; submodularity; current research trends.


Start End Topics & Lecture Notes Required Readings Additional Resources
Jan 5 Jan 14 Introduction AIMA Chapter 1
Beyond Turing Test
Applications of AI
Benefits/Risks of AI
Jan 15 Jan 21 Uninformed search AIMA Chapter 3.1-3.4
Beam Search
Intuition of Search Algorithms
Search Algorithms Performance
Jan 21 Jan 22 Informed search AIMA Chapter 3.5-3.7
Depth First Branch and Bound
A*/IDA* Example
Pattern Databases
Jan 24 Feb 10 Programming Assignment 1
Format Checker
Jan 28 Jan 29 Constraint Satisfaction AIMA Chapter 6 (skip 6.3.3)
Conversion to Binary CSP
Feb 2 Feb 5 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)
Feb 5 Feb 11 Logic and Satisfiability AIMA Chapter 7, 8.1-8.3
Feb 12 Feb 19 Advanced Satisfiability Advanced SAT Solvers (Sections 2.3, 2.4)
Phase Transitions
Feb 16 Mar 1 Programming Assignment 2
Format Checker
Problem Generator
Feb 23 Feb 26 Adversarial search AIMA Chapter 5
How Intelligent is Deep Blue?
Minimax Applet
Poker is Solved!
Mar 4 Mar 18 Programming Assignment 3
Starter Code
Mar 9 Mar 9 Agent Architectures AIMA Chapter 2

Mar 11 Mar 12 Intro to Probability AIMA Chapter 13
History of Bayes Theorem
Mar 12 Mar 23 Bayesian Networks Representation AIMA Chapter 14.1-14.4
Influence Flow in Bayes Nets
Mar 25 Mar 26 Bayesian Networks Inference and Learning AIMA Chapter 14.5, 20
Log Probabilities
Mar 26 Apr 9 Programming Assignment 4

Mar 31 Mar 31 Decision Theory AIMA Chapter 16.1-16.3, 16.6

Apr 1 Apr 6 Markov Decision Processes AIMA Chapter 17.1-17.4

Apr 8 Apr 13 Reinforcement Learning AIMA Chapter 21.1-21.3
TD Learning for Backgammon
Convergence of Q learning
Apr 10 Apr 24 Programming Assignment 5
Apr 15 Apr 16 Bandits and Monte Carlo Tree Search
UCT for Games
Monte Carlo Planning (Sections 3.1-3.3)
AIMA Chapter 21.4
UCT for Go
Monte Carlo Planning
Apr 20
Apr 20
Intro to NLP
Future of Web Search,
IBM Watson Deep QA
Apr 22
Apr 29
Machine Learning Intro and Algorithms AIMA Chapter 18.1-18.4, 18.6-18.6.3,
18.7-18.7.3, 18.8-18.8.1, 18.9


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 5-6 (mostly 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.