Mathematics of machine learning

Lectures: 2 sessions / week, 1.5 hours / session

18.100C Real Analysis

18.06SC Linear Algebra

18.05 Introduction to Probability and Statistics

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 class will be split in three main parts:

There are 3 homework assignments.

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).

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