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3rd May 2016, 05:01 PM
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Join Date: Apr 2013
Re: Artificial Intelligence IITK

The Indian Institute of Technology Kanpur is a public research college located in Kanpur, Uttar Pradesh. The Institute was established in 1959. It was declared to be Institute of National Importance by Government of India under IIT Act.

IIT Kanpur CS365 Artificial Intelligence course

Text

AI: A Modern Approach, Stuart Russell and Peter Norvig, 2nd ed

Instructor
Amitabha Mukerjee

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: 35%
Course Discussions, Homework and Labs: 15-20%
Final Project: 45-50%
Proposal: 5%
Presentation: 10%
Report: 15%
Demo/Oral: 20%
(approximately)

Projects

To be done in teams of 1 or 2 persons.
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.
Owing to the high project weightage, project groups will be formed based on interest area groups based on the paper selected for review.

Course Topics

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


INTRO: AI objectives and methods; agents: symbolic systems, learning systems
TURN to Learning: History of AI, Agent models:
- Programmed intelligence : Symbolic processing - Learned behaviours: Abstracting from data
- Embodied AI - developing symbols.
UNCERTAINTY : Probability; Classification and Regression; Probabilistic models; Information theory
Mini-Workshop: State of AI : Proposal Presentations focusing on background and motivation
LEARNING: Decision Trees; Clustering; Generative vs Discriminative; Manifold Dimensionality Reduction; STATISTICAL LEARNING. Naive Bayes; k-NN; multi-layer perceptron; kernel methods & SVM; Deep Learning;
SENSING: Vision - Image Formation, Gradient and Motion cues, Learning Backgrounds, Tracking
ACTION: Robotics - articulated and mobile robots; motion planning, task planning.
SEARCH, CONSTRAINT PROPAGATION, and LOGIC:
LANGUAGE: natural language processing fundamentals
PROJECTS: proposal, presentations, final demos
CLOSURE: cognitive science, philosophy, and the future

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

Contact address

Indian Institute of Technology Kanpur
Kalyanpur
Kanpur, Uttar Pradesh 208016


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