ELL409: Machine Intelligence and Learning
Instructor: Seshan Srirangarajan
Lecture Timing:
Day/Time: Mon, Tue, Fri 12:00 pm - 12:50 pm (Slot J)
Room: Online
Teaching Assistants:
Abhinav Gaur (eet192237 AT ee.iitd.ac.in)
Panchal Sanket Hitendrakumar (eet192347 AT ee.iitd.ac.in)
Vidushi Jain (eet192356 AT ee.iitd.ac.in)
Srinivas Anjay Kumar Nyayapati (jtm192169 AT dbst.iitd.ac.in)
Abhilash Gaur (tiz208008 AT dbst.iitd.ac.in)
Pre-requisites: MTL106, COL106
The course is open to all suitably inclined B.Tech. students of all disciplines, who satisfy the pre-requisites listed above.
Reference Books:
[Bishop] Pattern Recognition and Machine Learning by C. M. Bishop, 1st Edition, 2006 (2nd Indian Reprint, 2015).
[Flach] Machine Learning: The Art and Science of Algorithms that Make Sense of Data by P. Flach, 1st Edition, 2012.
[RN_Book] Artificial Intelligence: A Modern Approach by S. Russell and P. Norvig, 3rd Edition, 2014.
[AML_Book] Learning from Data: A Short Course by Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, 1st Edition, 2012.
Other References:
[Stanford_CS229] Machine Learning Course (CS229), Stanford University, Course Materials / Handouts.
P. Domingos, A Few Useful Things to Know about Machine Learning, Communications of the ACM, Vol. 55, No. 10, pp. 78-87, 2012.
Course Grading:
Minor: 30 %
Programming Assignments: 30 %
Major: 40 %
Exam Schedule:
Minor: 17 Mar 2021 (Wed)
Major: 11 May 2021 (Tue)
Course Outline:
S. No. |
Topics |
Lectures |
References |
1. |
Introduction to the Course |
1 |
|
2. |
Basics of Learning: Learning Model, Theory of Generalisation |
2-11 |
[AML_Book: Chap 1], [AML_Book: Chap 2] |
3. |
Linear Models for Regression |
12, 15 |
[AML_Book: Chap 3] |
4. |
Logistic Regression |
13-14 |
[AML_Book: Chap 3] |
5. |
Model Selection: Regularization, Validation |
16-18 |
[AML_Book: Chap 4] |
6. |
Neural Networks |
19-22 |
[Stanford_CS229: Deep Learning] |
7. |
Support Vector Machines |
23 |
[AML_Book: Chap 8] |
8. |
Feature Selection |
xx-xx |
|
9. |
Combining Models |
xx-xx |
|
10. |
Unsupervised learning: K-Means, GMM, EM |
xx-xx |
|
11. |
Eigenanalysis: PCA, LDA, Subspaces |
xx-xx |
|
|