E0-270: Machine Learning (Spring 2025)

Logistics

Class Meetings: Mondays and Wednesdays, 11:00 AM - 12:30 PM, CSA 117
Tutorial/discussion sessions will be scheduled on an on-going basis; Teams Link: t1y87g2

Instructor: Prof. Ambedkar Dukkipati (ambedkar [at] iisc)

Teaching Assistants:

• Arpana Alka (arpanaalka [at] iisc)
• Soumyadeep Roy (rsoumyadeep [at] iisc)
• O Prabhas Reddy (prabhasreddy [at] iisc)
• Naveen Kumar P (kpnaveen [at] iisc)
• Parag Dutta (paragdutta [at] iisc)

Course Evalutaion:

• Midterms: 2*15%
• Assignments: 2*10%
• Final examination: 50%

Course Description

With the increasing amounts of data being generated in diverse fields such as astronomical sciences, health and life sciences, financial and economic modeling, climate modeling, market analysis, and even defense, there is an increasing need for computational methods that can automatically analyze and learn predictive models from such data. Machine learning, the study of computer systems and algorithms that automatically improve performance by learning from data, provides such methods; indeed, machine learning techniques are already being used with success in a variety of domains, for example in computer vision to develop face recognition systems, in information retrieval to improve search results, in computational biology to discover new genes, and in drug discovery to prioritize chemical structures for screening. This course aims to provide a sound introduction to both the theory and practice of machine learning, with the goal of giving students a strong foundation in the subject, enabling them to apply machine learning techniques to real problems, and preparing them for advanced coursework/research in machine learning and related fields.

Topics

• Introduction and Overview
• Data Representation and Model Variants
• Supervised Learning and Bayesian Decision Theory
• Linear and Logistic Regression
• Probabilistic Perspective of Linear and Logistic Regression
• Perceptron and Error Based Update Rule
• Support Vector Machines and Kernels
• Introduction to Neural Networks
• Dimensionality Reduction and PCA
• Introduction to Reinforcement Learning
• Expectation Maximization and Mixture Models
• Introduction to Learning Theory and PAC

Demonstrations

• Distance based models (k-Nearest Neighbors and Discriminant Analysis) - PD
• Linear and Logistic Regression - PD
• Support Vector Machines - PD
• Introduction to Neural Networks and Image Classification - AA
• Introduction to Reinforcement Learning - PD

Additional Information

Preferred Background
E0 232: Probability and Statistics (or equivalent course elsewhere) and earned a grade of B or higher. In addition, some background in linear algebra and optimization will be helpful.

Academic Honesty
As students of IISc, we expect you to adhere to the highest standards of academic honesty and integrity.
Elements of the course are designed to support your learning of the subject. Copying will not help you (in the exams or in the real world), so don't do it. If you have difficulties learning some of the topics or lack some background, try to form study groups where you can bounce off ideas with one another and try to teach each other what you understand. You're also welcome to talk to any of us and we'll be glad to help you.
If any exam/report is found to be copied, it will automatically result in a zero grade for that exam/assignment and a warning note to your advisor. Any repeat instance will automatically lead to a failing grade in the course.

Materials:

• Recommended Textbooks:
1. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
2. Hastie T, Tibshirani R and Friedman J, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition, 2009
3. Haykin. S, Neural Networks and Learning Systems, Prentice-Hall, 3rd Edition, 2009 Goodfellow, Bengio, Courville, Deep Learning, MIT Press, 2017
• Additional Textbooks:
1. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2nd Edition, 2009.
2. T. Mitchell, Machine Learning. McGraw Hill, 1997.
• Optimization Textbooks:
1. Fletcher R., Practical Methods of Optimization, John Wiley, 2000.
2. David G. Luenberger and Yinyu Ye, Linear and Nonlinear Programming, Springer
3. Yurii Nesterov,Introductory Lectures on Convex Optimization, Springer
4. Stephen Boyd,Convex Optimization, Cambridge University Press