CSE 446, Winter 2018

Machine Learning


Instructor: Sham Kakade

TAs: Kousuke Ariga, Benjamin Evans, Xingfan Huang, Sean Jaffe, Vardhman Mehta, Patrick Spieker, Jeannette Yu, Kaiyu Zheng.

Contact: cse446-staff@cs.washington.edu

PLEASE COMMUNICATE TO THE INSTUCTOR AND TAs ONLY THROUGH THIS EMAIL (unless there is a reason for privacy in your email).

Discussion: Canvas discussion board


Class lectures: MWF 9:30-10:20am, Room: SIG 134

Office Hours:

***Please double check the website before you arrive for location changes/cancellations.***

Kousuke Ariga: Wednesday 1:30-2:30pm 2nd floor breakout

Benjamin Evans: Tuesday 9:30-10:30am CSE 021

Xingfan Huang: Tuesday 11:00-12:00pm CSE 021

Sean Jaffe: Thursday 2:00-3:00 pm CSE 007

Sham Kakade: Mon 3:15-4:15, CSE 436

Vardhman Mehta: Friday 2:30-3:30pm CSE 007

Patrick Spieker: Thursday 12:30pm-1:20pm CSE 021

Jeannette Yu: Wednesday 11:30am-12:30pm CSE 021

Kaiyu Zheng: Monday 11:00-12:00pm CSE 021


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 (e.g. python) and should have a pre-existing working knowledge of probability, statistics, algorithms, and linear algebra.


Discussion Forum and Email Communication

IMPORTANT: All class announcements will be broadcasted using Canvas. Please send questions about homeworks, projects and lectures to the Canvas discussion board . If you have a question of personal matters, please email the instructors list: cse446-staff@cs.washington.edu.


Material and textbooks

The primary reading assignments will be from the following two books:

  • A Course in Machine Learning, Hal Daume.
  • Machine Learning: A Probabilistic Perspective, Kevin Murphy.
  • Other helpful textbooks are:

  • From a more theoretical perspective: Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David.
  • More statistical: The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, Jerome Friedman.
  • A little more Bayesian: Pattern Recognition and Machine Learning, Chris Bishop.
  • From an AI angle: Machine Learning , Tom Mitchell.

  • Policies

    Grades will be based on five assignments (40%), a midterm (20%), and a final (40%). In a small number of cases, grades may be adjusted after this breakdown, e.g. grades will (significantly) drop based on failure to submit all the HWs; grades may go up for particularly remarkable exam scores; grades may go up for consistently remarkable homeworks.

    Exams:

    If you are not able to make the exam dates (and do not have an exception based on UW policies), then do not enroll in the course. Exams will not be given on alternative dates.

    Homeworks:

    Homework must be done individually: each student must hand in their own answers. In addition, each student must submit their own code in the programming part of the assignment (we may run your code). It is acceptable for students to discuss problems with each other; it is not acceptable for students to look at another students written answers. It is acceptable for students to discuss coding questions with others; it is not acceptable for students to look at another students code. You must also indicate on each homework with whom you collaborated with.

    We expect the students not to copy, refer to, or seek out solutions in published material on the web or from other textbooks (or solutions from previous years or other courses) when preparing their answers. Students are certainly encouraged to read extra material for a deeper understanding. If you do happen to find an assignment's answer, it must be acknowledged clearly with an appropriate citation on the submitted solution.

    HW LATE POLICY: Homeworks must be submitted by the posted due date. You are allowed up to 2 LATE DAYs for the homeworks throughout the entire quarter, which will automatically be deducted if your assignment is late. In particular, for any day in which an assigment is late by up to 24 hours, then one late day will be used (up to two late days). After two of the late days are used up, any assignment turned in late will incur a reduction of 33% in the final score, for each day (or part thereof) if 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.

    Academic and Personal Integrity

    The instructor expects (and believes) that each student will conduct himself or herself with integrity. While the TAs will follow the course and university policies with regards to grading and proctoring, it is ultimately up to you to conduct yourself with academic and personal integrity for a number of important reasons.


    Diversity and Gender in STEM

    While many academic disciplines have historically been dominated by one cross section of society, the study and participation of STEM disciplines is a joy that the instructor hopes that everyone can pursue. It is not obvious to the instructor what the best solution is. At the least, the instructor encourages students to both be mindful of these issues and, in good faith, try to take steps to fix them. You are the next generation here.


    Readings

    The required readings are for your benefit and they encompass material that you are required to understand. The extra reading is provided to give you additional background. Please do the required readings before each class.


    Section Materials


    Lecture Notes and Readings