COL333/COL671 - Artificial Intelligence - Autumn 2019
Tuesday, Thursday, Friday 11-11:50 pm in LH 111
(mausam at cse dot iitd dot ac dot in)
Office hours: by appointment, SIT 402
| TAs: (office hours by appointment)
|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
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
|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 20||Aug 29||Adversarial search||AIMA Chapter 5
How Intelligent is Deep Blue?
|Aug 30||Sep 4||Constraint Satisfaction||AIMA Chapter 6 (skip 6.3.3)
Conversion to Binary CSP
|Aug 30||Sep 16||Programming Assignment 2|
|Sep 4||Sep 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)
|Sep 17||Sep 17||Intro to Probability||
AIMA Chapter 13
History of Bayes Theorem
|Sep 19||Oct 10||Programming Assignment 3||Resources|
|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
|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 29||Nov 1||Reinforcement Learning||
AIMA Chapter 21.1-21.3
||TD Learning for Backgammon
|Nov 5||Nov 8||Introduction to Deep Learning and CNNs||
DL 6, 9.1-9.3
|Nov 13||Nov 13||Deep Reinforcement Learning||
Deep Q Networks
|Nov 13||Nov 14||Ethics of AI|
|Nov 14||Nov 14||Wrap Up|
Stuart Russell & Peter Norvig,
Artificial Intelligence: A Modern Approach,
Prentice-Hall, Third Edition (2009) (required).
Ina GoodFellow, Yoshua Bengio & Aaron Courville,
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.
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.
- The Kyunki Saas Bhi Kabhi Bahu Thi Rule: This rule says that you are free to meet with fellow students(s) and discuss assignments with them. Writing on a board or shared piece of paper is acceptable during the meeting; however, you should not take any written (electronic or otherwise) record away from the meeting. This applies when the assignment is supposed to be an individual effort or whenever two teams discuss common problems they are each encountering (inter-group collaboration). After the meeting, engage in a half hour of mind-numbing activity (like watching an episode of Kyunki Saas Bhi Kabhi Bahu Thi), before starting to work on the assignment. This will assure that you are able to reconstruct what you learned from the meeting, by yourself, using your own brain.
- The Right to Information Rule: To assure that all collaboration is on the level, you must always write the name(s) of your collaborators on your assignment. This also applies when two groups collaborate.