Introduction to Machine Learning

Practical information

• Lectures: Monday and Wednesday, 9:00AM to 10:20AM

• Location: Doherty Hall A302

• Recitations: Thursday, 5:00PM to 6:00PM

• Location: Wean Hall 5302

• Instructor: Alex Smola

• TAs: Jay-Yoon Lee, Jin Sun, Di Xu, Zhou Yu, and Manzil Zaheer

• Grading Policy: Homework (33%), Midterm (17%), Final (17%), Project (33%).

Office Hours

If you cannot get things resolved after the lecture, you can make an appointment for further office hours. Also, you should discuss your project with the TAs.

Overview

Machine learning studies the question “how can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that learn to better understand your speech based on experience listening to you.

This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from machine learning, classical statistics, data mining, Bayesian statistics and information theory. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.

Video Feed

Lecture

Project Presentations

Note that some of the projects have audio problems or may be missing the first or last few seconds. This is due to the way how the video was being recorded. It cannot be fixed.

Prerequisites

• Basic probability and statistics are a plus.

• Basic linear algebra (matrices, vectors, eigenvalues) is a plus. Knowing functional analysis would be great but not required.

• Ability to write code that exceeds 'Hello World’. Preferably beyond Matlab or R.

• Basic knowledge of optimization. Having attended a convex optimization class would be great but the recitations will cover this.

• You should have no trouble answering the questions of the self evaluation handed out for the 10-601 course.

Updates

• 011815 Initial site update