IE613: Online Machine Learning, Jan-April 2021
Aim of this course is to study learning algorithms and analyze their performance.
Lecture Hours
- Monday 5.30-7pm
- Thursday 5.30-7pm
Location
Online Using MS Teams
Teaching Assistants
- Fehmina Malik
- Debamita Ghosh
- Hitesh Gudwani
- Harshit Pandey
TA Hours
Timings: Every Monday 2-4pm
Venue : Room 201, IEOR building
Syllabus
Introduction to batch learning: Empirical Rsik Minimization, PAC learning.
Online learning: adversarial and stochastic settings, online learning with expert feedback, bandit feedback.
Multi-Armed Bandits: Algorithms for simple and cumulative regret (expected and high probability), and their analysis.
Contextual Bandits
Pure exploration
Course Grades
- 20 points: Midterm
- 35 points: 4 Assingments
- 15 points: Pre-project report
- 30 points: Final project
References
- S. Shalev-Shwartz and S. Ben-David, ``Understanding Machine Learning:From Theory to Algorithms,'' Cambridge University Press, 2014,
Download.
- T. Hastie, R. Tibshirani and J. Friedman, ``Elements of statistical machine learning,'' Springer series in statistics, 2009,
Download.
- S. Shalev-Shwartz, ``Online Learning and Online Convex Optimization,''
NOW publisher, 2012,Download.
- S. Bubeck and N. Cesa-Bianchi, ``Regret Analysis of Stochastic and Nonstochastic Multi-armed
Bandit Problems,'' NOW publisher, 2012,
Download.
- N. Cesa-Bianchi and G. Lugosi, ``Prediction, Learning, and Games, Cambridge University Press, 2006,
Download.
- T. Lattimor and C. Szepesvari, ``Bandit Algorithms, Cambridge University Press,
Download.
Course Gardes
Assignment 1.
Assignment 2.
Assignment 3.
Assignment 4.