CS365: Artificial Intelligence

Department of Computer Science & Engineering, IIT Kanpur

Jan - Apr 2013

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Course Information

Text

AI: A Modern Approach, Stuart Russell and Peter Norvig, 2nd ed
(See below for additional readings)

Prerequisites

ESO 211 Data Structures. Optional: Probability and Statistics, familiarity with logic.

Course Objective

Artificial Intelligence tries to have machines do things that are normally associated with intelligence in humans. In the process, this also sheds light on the processes of human cognition.

This course will introduce these topics through lectures, class presentations and discussions, and most importantly, through projects. Each of you is also expected to select a project in which you will investigate some topic of current research interest, and you are expected to be able to communicate the key ideas of your project to others in the course.

Grading Scheme

  • Two Written Exams: 40%
  • Course Discussions, Homework and Labs: 10-15%
  • Final Project: 45-50%
    • Proposal: 5%
    • Presentation: 10%
    • Report: 15%
    • Demo/Oral: 20%
    (approximately)

Projects

  • Projects from past years, assignments, solutions, and other details may be seen here.
  • Some projects with a Machine Learning focus which can be found here may also be relevant.
  • Owing to the high project weightage, project groups will be formed by lottery within the first two weeks of class.

Course Topics

For some topics, there will be additional presentations by students.

Topic Week Ref
INTRO: AI objectives and methods; agents: symbolic systems, learning systems week 1 ch.1,2
TURN to Learning: History of AI, Agent models:
- Programmed intelligence : Symbolic processing - Learned behaviours: Abstracting from data
- Embodied AI - developing symbols. 		week 2 book ch.2, ch.18
UNCERTAINTY : Probability; Classification and Regression; Probabilistic models; Information theory week 3 Ch. 13, Bishop ch.1, 18.2
LEARNING. Naive Bayes; Decision Trees; Generative vs Discriminative; SVM; Deep Learning; Manifold Dimensionality Reduction week 4-5 ch. 13,18,20; Duda Hart ch.2; Bishop ch.1;
SENSING: Vision - Image Formation, Gradient and Motion cues, Learning Backgrounds, Tracking

 week 6 ch.24,
marr.1
ACTION: Robotics - articulated and mobile robots; motion planning, task planning.

 week 7 ch.25
SEARCH and CONSTRAINT PROPAGATION: week 8 ch.3,5,6
OVERVIEW: language, philosophy, and the fuure week 9 ch.22, 23 ; ch. 26
PROJECTS: proposal, presenttations, final demos week 10-12 ch.3,5,6

Additional Readings

  • Marr, David; Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information, W.H. Freeman, 1982, 397 pages, ISBN 0716715678
  • Bishop, Christopher M.; Pattern Recognition and Machine Learning, Springer, 2006, 738 pages, ISBN 0387310738
  • Richard O. Duda; Peter E. Hart; David G. Stork; Pattern Classification (2/e), Wiley-India, 2007, 676 pages, ISBN 8126511168
  • Lee, John A.; Michel Verleysen; Nonlinear Dimensionality Reduction, Springer 2007, 310 pages, ISBN 0387393501
  • Research Papers