CSE 546: Machine Learning

CSE 546, Autumn 2017 Machine Learning

Lecture: Tuesday, Thursday 11:00-12:20 Room: MUE 153

Instructor: Professor Kevin Jamieson

Contact: cse546-instructors@cs.washington.edu

Discussion: Canvas discussion board, Slack (invite link on Canvas Discussion, or request via email directly above if not registered)

TAs: Dae Hyun Lee, Yao Lu, Aravind Rajeswaran, Nancy Wang

Office Hours (check discussion board for exceptions):

About the Course and Prerequisites

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 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 optimization.

Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra, probability, statistics and algorithms. For a brief refresher you may consult:

Textbook

The required textbook will be (should be available at U Bookstore by start of class):

  • Machine Learning: A Probabilistic Perspective, Kevin Murphy.

    • Material in the optional textbooks may also be helpful. All of the following are either free on the authors' webpages or are available to UW students on the campus network.

  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. Aesthetically beautiful with plenty of algorithms, examples, theory, and intuition.
  • Computer Age Statistical Inference: Algorithms, Evidence and Data Science, Bradley Efron, Trevor Hastie. Includes material like the Bootstrap and false-discovery-rate control that other books overlook.

    • Machine learning is a marriage of statistics and algorithms. Algorithms more often than not involve an optimization program, thus the following resources may be useful:
  • Numerical Optimization, Nocedal, Wright (must be on UW network to access Springerlink). Practical algorithms and advice for general optimization problems.
  • Convex Optimization: Algorithms and Complexity, Sébastien Bubeck. Elegant proofs for the most popular optimization procedures used in machine learning.

    • Discussion Forum and Email Communication

    IMPORTANT: All class announcements will be broadcasted using the Canvas 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.

    We are experimenting with Slack this quarter. An invite link is available on the Canvas Discussion board. If not registered, please request an invite link by sending an email to cse546-instructors@cs.washington.edu. Slack lowers the barrier to asking for help and encourages more interaction. It is also a place where students who are not registered can interact with the rest of the class (unlike Canvas)

    Please send all questions to Slack or the discussion 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.

    Grading and Evaluation

    Your grade will be based on 5 homework assignments (65%) and a final project (35%).

    Homework

    Your homework score will be the smaller of 100 points and the cumulative number of points you receive on the assignments. The first homework is worth 10 points, and the final four are worth 25 each. This means if you receive grades $(x_0,x_1,x_2,x_3,x_4)$ you will receive a score of $\min(100, x_0+x_1+x_2+x_3+x_4)$. In particular, if you receive grades

    Homeworks must be submitted by the posted due date at 11:59 PM Seattle time.

    Each homework assignment contains both theoretical questions and will have programming components.

    The first homework (10 points) is designed to be very easy and its purpose is to get you comfortable with Python and Latex. There will be generous office hours for assistance.

    COLLABORATION POLICY: Homework must be done individually: each student must hand in their own answers. In addition, each student must write and submit their own code in the programming part of the assignment (we may run your code). It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. You must also indicate on each homework with whom you collaborated.

    RE-GRADING POLICY: All grading related requests must be submitted to the TA via email only. Office hours and in person discussions are limited solely to asking knowledge related questions, not grade related questions. If you feel that we have made an error in grading your homework, please let us know with a written explanation, and we will consider the request. Please note that regrading of a homework may cause your grade to go up or down on the entire homework set.

    LATE POLICY: Homeworks must be submitted by the posted due date. There is no credit for late work. The homework scoring system of above is an attempt to minimize the harshness of this policy.

    NO EXCEPTIONS WILL BE GIVEN TO THE GRADING POLICIES (unless based on university policies, e.g. medical reasons). IF YOU ARE NOT ABLE TO COMPLY WITH THE LATE HOMEWORK POLICY, DUE TO TRAVEL, CONFERENCES, OTHER DEADLINES, OR ANY OTHER REASON, DO NOT ENROLL IN THE COURSE.

    HONOR CODE: 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 students 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. For more information, please see the CSE Academic Misconduct policy that this course adheres to.

    Project

    You will work independently or with a partner on a machine learning project spanning most of the quarter ending with a poster presentation and written report. You may use techniques developed in this course but are also encouraged to learn and apply new methods. The project should address a novel question with a non-obvious answer and must have a real-data component. We will provide some seed project ideas. You can pick one of these ideas, and explore the data and algorithms within and beyond what we suggest. You can also use your own data/ideas, but, in this case, you have to make sure you have the data available at the time of the proposal and a nice roadmap, since a quarter is too short to explore a brand new concept. The components of the project are

    Example project ideas can be found here.

    Homework


    Important Dates

    Date Deliverable Due
    10/5 Homework 0
    10/17 Homework 1
    10/24 Project proposal
    11/2 Homework 2
    11/14 Project milestone
    11/21 Homework 3
    12/5 Homework 4
    12/7 Poster presentation
    12/7 Project report due
    12/14 Project Reviews due

    Schedule