ELL409: Machine Intelligence and Learning
Instructors: Sumantra Dutta Roy and Seshan Srirangarajan
Lecture Timing:
Day/Time: Tue, Wed, Fri 08:00 - 08:50 am (Slot C)
Room: LH-308 (Lecture Hall Complex)
Teaching Assistants:
Amanpreet Kaur (eet142441 AT ee.iitd.ac.in)
Meghna Pippal (eet142447 AT ee.iitd.ac.in)
Pre-requisites: MTL106, COL106
No one shall be permitted to audit the course. People are welcome to sit through it, however. The course is open to all suitably inclined B.Tech students of all disciplines, who satisfy the pre-requisites listed above (which can be done concurrently with this course, in the worst case).
This is a Departmental Elective (DE). This will also be treated as a Departmental Elective (DE) for students governed under the old curriculum.
Course Textbook:
[Bishop] Pattern Recognition and Machine Learning by C. M. Bishop, 1st Edition, 2006 (2nd Indian Reprint, 2015).
Reference Books:
[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.
[ML_Class] Machine Learning Course, Stanford University, Course Notes, Fall 2011.
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-I: 20 %
Minor-II: 25 %
Assignments: 24 %
Major: 31 %
Exam Schedule:
Minor-I: LH 325, 14 February 2016 (Sunday), 11:00 am - 12:00 noon
Minor-II: LH 325, 23 March 2016 (Wednesday), 11:00 am - 12:00 noon
Major: LH 325, 05 May 2016 (Thursday), 1:00 pm - 3:00 pm
Course Outline:
S. No. |
Topics |
Lectures |
Instructor |
References |
1. |
Unsupervised Learning: K-Means, Gaussian Mixture Models, EM |
1-10, 14 (2 hour extra class) |
SDR |
[Bishop: Chap 9] |
2. |
Basics of Learning: Learning Model, Theory of Generalisation |
11-13, 15-16 |
SSR |
[AML_Book: Chap 1], [AML_Book: Chap 2] |
3. |
Linear Models for Regression |
17-24 |
SDR |
[Bishop: Chap 3], [Bishop: Chap 4] |
4. |
Eigenanalysis: PCA, LDA and Subspaces |
25-27 |
SDR |
[Bishop: Chap 12] |
5. |
Logistic Regression |
28-29 |
SSR |
[ML_Class: Section 6] |
6. |
Neural Networks |
30-33 |
SSR |
[ML_Class: Section 8], [ML_Class: Section 9] |
7. |
Model Selection |
34 |
SSR |
[ML_Class: Section 10] |
8. |
Support Vector Machines |
35-37 |
SSR |
[ML_Class: Section 12] |
9. |
Feature Selection |
38-39 |
SSR |
[Flach: Chap 10] |
10. |
Combining Models |
40-41 |
SSR |
[Flach: Chap 11] |
11. |
Graphical Models |
xx-xx |
SSR |
|
12. |
Deep Learning |
xx-xx |
SSR |
|
13. |
Basics of Artificial Intelligence (AI) |
xx-xx |
SSR |
|
|