Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Prerequisites
18.05 Introduction to Probability and Statistics
Description
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
The Topics Covered
The class will be split in three main parts:
 The Statistical Theory of Machine Learning.
 Classification, Regression, Aggregation
 Empirical Risk Minimization, Regularization
 Suprema of Empirical Processes
 Algorithms and Convexity.
 Boosting
 Kernel Methods
 Convex Optimization
 Online Learning.
 Online Convex Optimization
 Partial Information: Bandit Problems
 Blackwell’s Approachability
Grading
ACTIVITIES  PERCENTAGES 

Assignments  40% 
Final Project  50% 
Lecture Notes Scribing  10% 

Homework 40%
There are 3 homework assignments.

Final project 50%
The final project should be in any area related to one of the topics of the course or use tools that are developed in class. Examples include: implementing an algorithm for real data, extend an existing method or prove a theoretical result (or a combination of these). You will need to submit a written report (~10 pages) and give a presentation in class in the last week of semester (the duration will depend on the size of the class).

Lecture notes scribing 10%