ELL784: Introduction to Machine Learning Instructors: Sumantra Dutta Roy and Seshan Srirangarajan Lecture Timing: Day/Time: Mon, Wed, 11:00 - 11:50 am, and Thu 12:00 - 12:50 pm (Slot H) Room: IIA-101 (Bharti School) Teaching Assistants: Devyani Agarwal (eet182556 AT ee.iitd.ac.in) Asmita Nandkumar Patil (eet182560 AT ee.iitd.ac.in) No one shall be permitted to audit the course. People are welcome to sit through it, however. This course is not open to B.Tech and Dual Degree students, who are supposed to opt for ELL409 (Machine Intelligence and Learning). Students will be permitted to earn credits for only one of the following overlapping courses: ELL409, EL784, COL341 (Fundamentals of Machine Learning), and COL774 (Machine Learning). 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. [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-I: 25 % Minor-II: 25 % Assignments: 25 % Major: 50 % Exam Schedule: Minor-I: LH-408, 04 February 2020 (Tuesday), 1:00 pm - 2:00 pm Minor-II: LH-408, 16 March 2020 (Monday), 1:00 pm - 2:00 pm Major: 03 September 2020 (Thursday), 10:00 am - 12:00 pm Course Outline: S. No. Topics Lectures Instructor 1. Unsupervised Learning: K-Means, Gaussian Mixture Models, EM 1-5 SDR 2. Eigenanalysis: PCA, LDA, and Subspaces 6-9 SDR 3. Linear Models for Regression 10-12 SDR 4. Support Vector Machines 13-21 SDR 5. Logistic Regression 22 SSR 6. Neural Networks 23-27 SSR 7. Model Selection 28 SSR