CSE 546, Autumn 2015

Machine Learning

Instructor: Sham Kakade

TAs: Naozumi Hiranuma, Angli Liu, John Thickstun

Contact: cse546-instructors@cs.washington.edu

Discussion: Canvas discussion board

Class lectures: TTh 10:30-11:50am, MOR 230

Recitations (only on some weeks): Weds, 5:30-6:50 pm, MOR 230

Recitation for Final: Weds Dec 9th, 5:30-6:50 pm, MOR 230

Office Hours (Kakade): Fri, 10:00-10:50 am, CSE 436

Office Hours (Nao): Fri, 3:00-4:00 pm, CSE 220

Office Hours (Angli: Weds, 3:00-4:00 pm, CSE 220

Office Hours (John): Mon, 5:30-6:30 pm, CSE 220


Machine learning explores the study and construction of algorithms that can learn from data. This study combines ideas from both computer science and statistics. The study of learning from data is playing an increasingly important role in numerous areas of science and technology.

This course is designed to provide a thorough grounding in the fundamental methodologies, statistics, 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 algorithms.

Prerequisites: 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 mathematical background to catch up and fully participate.

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: cse546-instructors@cs.washington.edu. 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.

Material and (optional) textbooks

The course material will be primarily drawn from posted notes. Material in the following optional textbooks may be helpful:

  • Optional Textbook: Machine Learning: a Probabilistic Perspective , Kevin Murphy.
  • Optional Textbook: Pattern Recognition and Machine Learning , Chris Bishop.
  • Optional Textbook: The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman.
  • Optional textbook: Machine Learning , Tom Mitchell.

  • Grading

    You MUST be present at both the midterm and the final. The only exceptions will be for conference/workshop travel. No other exceptions will be made. If you are not able to make these dates, then do not take the class. The midterm is in class on Nov 5th. The final is at the scheduled university time: 10:30-12:20 Monday, Dec. 14, 2015. In addition to the below, there is up to 10% subjective room for increase in grades due to extra participation (e.g. in the discussion boards or in class) and for particularly impressive projects/homeworks.

  • Midterm (15%)
  • Homeworks (4 assignments 35%)
  • Final project (30%)
  • Final exam (20%)
  • Homework policy

    Important Note: As we sometimes 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 (referring to unauthorized material is considered a violation of the honor code). Similarly, we expect to not to google directly for answers. The homework is to help you think about the material, and we expect you to make an honest effort to solve the problems. If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution.

    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

  • Homeworks are due at the beginning of class, unless otherwise specified, through Catalyst.
  • Any assignment turned in late, will incur a reduction of 33% in the final score, for each day (or part thereof) it is late. For example, if an assignment is up to 24 hours late, it incurs a penalty of 33%. Else if it is up to 48 hours late, it incurs a penalty of 66%. And any longer, it will receive no credit.
  • You are allowed to use 3 LATE DAYs throughout the entire quater only for the homeworks. Please use these wisely, and plan ahead for conferences, travel, deadlines, etc.
  • You must turn in all 4 homeworks, even if for zero credit, in order to pass the course. (Empty homeworks do not count.)
  • 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.

    Project Page Link

    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.


    Recitations will only occur on some Weds, depending on interest. The schedule is here:


    Schedule and notes