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