Course Information
Text
AI: A Modern Approach, Stuart Russell and Peter Norvig, 3rd ed
(See below for additional readings)
(See below for additional readings)
Instructor
Prerequisites
CS210 / ESO 211 Data Structures and Algorithms; Probability and Statistics.
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.
Key material covered will cover the basics of search, regression and
generalized function learning, pattern recognition, logical reasoning.
We will also focus on applications in the areas of machine vision, natural
language processing, and robotics.
We will introduce these topics through lectures and homeworks in the
first part of the course. This will integrate class presentations
and discussions. This will be followed by projects, the topics for which
will be decided around week 6. The projects should 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.
As per consensus arrived at in day 1 in class, in the interest of
cohesion in the project groups, anyone wishing to drop needs to do so before
February 7. You will NOT be able to drop the course after the midsems.
Grading Scheme
- Two Written Exams: 15+20%
- Course Discussions, Homework and Labs: 15-20%
-
Final Project: 45-50%
- Proposal: 5%
- In-class Presentation / discussions: 10%
- Final poster / Demo: 15%
- Report: 20%
Projects
- To be done in teams of 1 or 2 persons (random assignment).
- Read and present a recent research paper from the list of suggested papers. Write a brief review. (around week 4)
- Define your project topic and present a project proposal. (around week 6)
- Implement the project over approximately six weeks.
- Projects from past years, assignments, solutions, and other details may be seen under the old course page.
Plagiarism policy
Plagiarism: Whenever you mention other people's work, there should be no possibility of a misunderstanding that the work was done as part of your project. As a first step, any text or figures being copied from any source must be clearly cited. All ideas used must be clearly attributed. Plagiarism in any form will result in at least a letter grade penalty and will be reported to the DOAA / SSAC. Significant plagiarism will result in an F grade.Course Topics
| Topic | Week | References |
|---|---|---|
| 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 |
| State of AI : Readings from current research | week 4 | Paper reviews based on list of papers |
| LEARNING: Regression vs Classification; Unsupervised vs Supervised; Clustering; Generative vs Discriminative; Manifold Dimensionality Reduction;STATISTICAL LEARNING. Naive Bayes; k-NN; multi-layer perceptron and deep Learning; kernel methods & SVM; | 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 ch.1 |
| ACTION: Robotics - articulated and mobile robots; motion planning, task planning. | week 7 | ch.25 |
| SEARCH, CONSTRAINT PROPAGATION, and LOGIC: | week 8 | ch. 3, 5, 6 |
| LANGUAGE: natural language processing fundamentals | week 9 | ch.22, 23 ; ch. 26 |
| PROJECTS: proposal, presentations, final demos | weeks 10-12 | Relevent research papers |
| CLOSURE: AI and cognition, philosophy | week 12b |
Additional Readings
- 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
- Research Papers