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Course Logistics

About the Course, Prerequisites and Grading

Machine learning explores the study and construction of algorithms that can learn from historical data and make inferences about future outcomes. This study is a marriage of algorithms, computation, and statistics so this class will be have healthy doses of each. The goals of this course are to provide a thorough grounding in the fundamental methodologies and algorithms of machine learning.

Prerequisites: Students entering the class should be comfortable with programming and should have a pre-existing working knowledge of linear algebra (MATH 308), vector calculus (MATH 126), probability and statistics (CSE 312/STAT390), and algorithms. For a brief refresher, we recommend that you consult the linear algebra and statistics/probability reference materials on the Textbooks page.

Grading: Your grade in the class will be based on 4 things.

  1. Homework Assignments: 5 homework assignments: HW0 (8%), HW1 (13%), HW2 (13%), HW3 (13%), HW4 (13%) worth a total of 60% of your grade.
  2. Midterm: 1 midterm exam worth 17% of your grade.
  3. Final: 1 final exam worth 20% of your grade.
  4. Section Participation: 3% of your grade. Section Participation can be accomplished by:
    • Attending section in-person, and being an active participant (attempting the problems, asking questions if you're stuck, etc.—this doesn't require volunteering to answer questions a certain number of times).

    We realize that there are going to be times this quarter where you may not be able to attend section. In the event you are unable to attend section for a given week, you may attempt the problems on your own and send your work to your section TA.

    Steps to follow:

    1. On the section calendar, find the problems listed in that week's section and the section handout.
    2. Attempt the problems listed, and write up your solutions. These solutions should not take as long as a homework problem. We don't need perfect formatting (they don't even need to be correct!). We want to see that you've done your best on the problem.
    3. Send an email to your TA(s) with a copy of your solutions.

    Note: To get credit for a section, you must email your TA(s) the problems by Sunday night at 11:59 PM (you cannot use late days on section problems).

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