CSE 546, Autumn 2017

Machine Learning

Instructor: Kevin Jamieson


Contact: cse546-instructors@cs.washington.edu

Discussion: Canvas discussion board

Class lectures: TTh 11:00-12:20 Room: MUE 153

Office Hours Changes: **Check message boards for the most up to date office hours.**

Office Hours (TAs): TBD

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 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 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. 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.


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 textbook may also be helpful (the authors have made the book available for free in PDF):

  • (Optional) The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman.