


VIDEO LECTURES:

Videos of class
lectures are
available, along with lectures slides,
homeworks, and exams. These are available to everyone for personal use, free of charge.
I hope you find these useful.


Course Description:

Machine Learning is concerned with computer programs that
automatically improve their performance through experience (e.g.,
programs that learn to recognize human faces, recommend music and
movies, and drive autonomous robots). This course covers the
theory and practical algorithms for machine learning from a
variety of perspectives. We cover topics such as Bayesian
networks, decision tree learning, Support Vector Machines,
statistical learning methods, unsupervised learning and
reinforcement learning. The course covers theoretical concepts
such as inductive bias, the PAC learning framework, Bayesian
learning methods, marginbased learning, and Occam's Razor. Short
programming assignments include handson experiments with various
learning algorithms, and a larger course project gives students a
chance to dig into an area of their choice. This course is
designed to give a graduatelevel student a thorough grounding in
the methodologies, technologies, mathematics and algorithms
currently needed by people who do research in machine learning.

Prerequisites:

Students entering the class are
expected to have a preexisting working knowledge of probability,
linear algebra, statistics and algorithms, though the class has been
designed to allow students with a strong numerate background to catch
up and fully participate. In addition, recitation sessions will be held
to review some basic concepts.

Textbook:

 Machine Learning, Tom Mitchell. (optional)
 Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
 The Elements of Statistical Learning: Data Mining,
Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome
Friedman. (optional)

Grading:

 Midterm (25%)
 Homeworks (30%)
 Final project (20%)
 Final exam (25%)

Auditing:

To satisfy the auditing requirement, you must either:
 Do *two* homeworks, and get at least 75% of the points in each; or
 Take the final, and get at least 50% of the points; or
 Do a class project
 Like any class project, it must address a topic
related to machine learning and you must have started the project while
taking this class (can't be something you did last semester). You will
need to submit a project proposal with everyone else, and present a
poster with everyone. You don't need to submit a milestone or final
paper. You must get at least 80% on the poster presentation part of the
project.
Please, send the instructors an email saying that you will be auditing the class and what you plan to do.


