Class lectures: MW 9-10:20am, BAG 261. (Campus Map)

Recitations: Tuesdays, 5:30-7:00 pm, LOW101

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to provide a thorough grounding in the fundamental methodologies, technologies, mathematics and algorithms of machine learning. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics, and from statistical algorithmics.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.

Prerequisites: STAT 341, STAT 391, or equivalent, or permission of instructor.

Discussion forum

IMPORTANT: All class announcements will be broadcasted using the Catalyst discussion board. The same applies to questions about homeworks, projects and lectures. If you have a question of personal matters, please email the instructors list:
Otherwise, please send all questions to this board, since other students may have the same questions, and we need to be fair in terms of how we interact with everyone. Also, please feel free to participate, answer each others' questions, etc.



Homework policy

Important Note: As we often reuse problem set questions from previous years, covered by papers and webpages, we expect the students not to copy, refer to, or look at the solutions in preparing their answers. We expect students to want to learn and not google for answers. The purpose of problem sets in this class is to help you think about the material, not just give us the right answers. Therefore, please restrict attention to the books mentioned on the webpage when solving problems on the problem set. If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution. Reading unauthorized material will be considered cheating.

Collaboration policy

Homeworks will be done individually: each student must hand in their own answers. In addition, each student must write their own code in the programming part of the assignment. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. You also must indicate on each homework with whom you collaborated.

Late homework policy

Homework regrades policy

If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation, and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.


You are expected to complete a final project for the class. This will provide you with an opportunity to apply the machine learning concepts you have learned. We will update the project requirements and due dates during the quarter.

Note to people outside UW

Feel free to use the slides and materials available online here. Please email the instructors with any corrections or improvements. Additional slides and software are available at the Machine Learning textbook homepage and at Andrew Moore's tutorials page.