Instructor: Ganesh Ramakrishnan
TAs: Namita Kalra, Raktim Chaki, Ritesh Kumar, Mayur Warialani, Mohit Agrawal, Ayush Maheshwari, Yash Shah, Manoj
TA Contacts
cs335-337@googlegroups.com
Video recordings, slides, references, tutorials, ipython notebook and all other course notes are all being made available on moodle.
List of applets
Sample papers for reading
Course Description
CS 337 provides a broad introduction to artificial intelligence and machine learning and various fields of application. The course is designed in a way to build up from root level.
Topics include:
- Supervised Classification (perceptron, support vector machine, loss functions, kernels, neural networks and deep learning)
- Supervised Regression (Least square regression, bayes linear regression)
- Unsupervised classification (clustering, expectation maximization)
- Introduction to learning theory (bias/variance tradeoffs).
The course will discuss the application of machine learning in devanagari script recognition which is a developing field in the machine learning community.
Class Timings, Venue and Grading
Time: Wednesdays and Fridays, 11:00-12:30 PM, Discussion to Solutions of Tutorial from previous week on Wednesdays
Venue: LA301
Office hours (for doubt clarifications): Tuesdays and Fridays 12:30 PM to 5 PM (preferably with prior email appointment) at my office SIA418, Fourth Floor, KReSIT
Credit Requirements25% Mid-semester exam 35% End semester exam 25% 2 Quizzes 5% In-class SAFE quizzes 10% Project No auditing is allowed this semester. Else, in the past, audit students had to submit assignments and project.
Prerequisites
- Basics of computer science including algorithms, data structure, complexity analysis etc.
- Basic Linear Algebra (course equivalent to MA 108 for BTech)
- Basic Probability Theory (course equivalent to CS 215 for BTech)
Eligibility
The Courses in Linear Algebra, Probability Theory will be helpful.
Primary books
(Main reference) Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag [ HTF]
- Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006. [ Bis07]
- Artificial Intelligence: A Modern Approach, by Stuart J. Russell and Peter Norvig, 3rd edition, Pearson, 2010
- Tom Mitchell, Machine Learning. McGraw-Hill, 1997
- Kevin Murphy, Statistical Machine Learning
- Understanding Machine Learning, Shai Shalev-Shwartz and Shai Ben-David, Cambridge University Press. 2017. Available online
Supplementary books
- [ PRS ]
- Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
- An Introduction to Probability Theory and Its Applications, Vol. 1, William Feller, 3rd edition, Wiley International
- Introduction to Probability Theory, Paul. G. Hoel, Sidney Port, Charles Stone
- [ BCAO]
- D. P. Bertsekas Convex Analysis and Optimization, Athena Scientific, 2003
[ BV ]- Boyd and Vandenberghe Convex optimization Book available online
- [ Han00]
- Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers
| ||
|
|