Introduction to Machine Learning

Practical information

• Lectures: Monday and Wednesday, 12:00PM to 1:20PM

• Location: Baker Hall A51

• Recitations: Tuesdays 5:00PM to 6:00PM

• Location: Porter Hall 100 (January 22, 2013), Doherty Hall A302 (January 29, 2013 onwards)

• Instructor: Barnabas Poczos (office hours 10am-12pm Thursdays in Gates 8231) and Alex Smola (office hours 2-4pm Tuesdays in Gates 8002)

• TAs: Ina Fiterau (office hours 2-4pm Mondays in Gates 8021), Mu Li (office hours 5-6pm Fridays in Gates 7713), Junier Oliva (office hours 4:30-5:30pm Thursdays in Gates 8227), Xuezhi Wang (office hours 5-6pm Wednesdays in Gates 6503), Leila Wehbe (office hours 10:30-11:30am Wednesdays in Gates 8021)

• Grading Policy: Homework (33%), Midterm (33%), Project (33%), Final (34%) with best 3 out of 4 used for score (final is mandatory).

• Google Group: Join it here. This is the place for discussions and announcements.

Updates

• 121212 Initial site update

• 011113 Significant admin updates and syllabus rearrangement

• 012413 Homework, lecture notes and recitations uploaded

• 020813 Homework, lecture notes and recitations updated

• 021813 Recitations updated

• 031913 Homework, lecture nodes updated

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. If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider Roni Rosenfeld's master's level course on Machine Learning, 10-601.

Resources

For specific videos of the class go to the individual lectures. This is also where you'll find pointers to further reading material etc.

• Lecture slides in Keynote 1 3 4 5 6 12 13 and PDF 1 2 3 4 5 6 8 9 10 11 12 13 14

• Recitation slides in Powerpoint 1 and PDF 1 2a 2b 3a 3b

• YouTube playlist (usually the first to get the updates)

• Problems 1 2 3 4

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.

Schedule