Aim of this course is to study learning algorithms and analyze their performance.

Lecture Hours

• Monday 5.30-7pm
• Thursday 5.30-7pm

Location

A1A2 classroom, Maths bldg., Ground floor

Teaching Assistants

• Arun Verma
• Fehmina Malik
• Mayuri Bakshi

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

• 25 points: Midterm
• 35 points: 4 Assingments
• 15 points: Pre-project report
• 25 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.