CS 760: Machine Learning (Spring 2017)

  • Instructor: David Page
    (Please put cs760 in email subject line; otherwise it's easy to overlook emails)
    Office Hours: 1pm-2pm Tuesdays and Thursdays in 1157 WID
  • TAs:
    Kirthanaa Raghuraman
    kraghuraman (at) wisc (dot) edu
    Office Hour: TBD

    Heemanshu Suri
    hsuri (at) wisc (dot) edu
    Office Hour: TBD

  • Important Dates:
    • Final Exam: TBD

  • Prerequisite: CS 540 or equivalent

  • Meeting Time and Location: 11am MWF, 132 Noland

  • Textbook:

    • Tom Mitchell (1997). Machine Learning. McGraw-Hill.
    • The following textbook is freely available for download and can be tested as alternative if you like: Shalev-Shwartz and Ben-David (2014). Let me know after the semester how it worked for you.

Course Overview

Many of the same technologies underly adaptive autonomous robots, scientific knowledge discovery, adaptive game playing and discovery from databases. This course will focus on these key underlying technologies, particularly supervised learning. The course will cover support vector machines, decision tree learners, neural network learning and Bayesian classifiers, among others. It also will address reinforcement learning and learning from relational data, including statistical relational learning and inductive logic programming. It will cover correct evaluation methodology, including case studies of methodological errors.

Course Outline

Course Requirements

The grading for the course will be be based on:

Homework Policy

The programming assignments are to be done individually. You may communicate with other class members about the problem, but please do not seek or receive help from people not in the class, and please do not share answers or code. Your programs may be in C, C++, Java, or Python. Other languages may be available on approval from the TAs. You must submit both linux executable and source code; your program should run on the CS Dept. lab computers. Assignments are to be submitted at the course moodle.

Homework assignments are due at the start of class on the assigned due date, and late homeworks will be penalized 20 points (out of 100) for each lecture that passes after the assigned due date. Homeworks cannot be submitted more than one week late; the submission site will be locked at that time. At the start of the course every student will be given 3 "free" days, each of which may be used to offset a 20-point late penalty. Only 1 free day can be used for any given written assignment, so that solutions can be posted at next class period. Free days are non-transferable, and no credit will be given for unused free days. Nevertheless, please use them sparingly because the late penalty is strictly enforced.

Assignments Weightage

  • Assignment 1 - 4%
  • Assignments 2 and 5 (Programming) - 10% each
  • Assignments 3 and 4 (Written) - 8% each
  • Homework Assignments

  • Assignment 1. Assigned 1/20, Due 1/29.
  • Assignment 2 Assigned 2/1, Due 2/15
  • Assignment 3 Assigned 2/16, Due 2/24
  • Assignment 4 Assigned 3/9, Due 3/18
  • Assignment 5 Assigned 4/4, Due 4/18

    Sample Exams

    Additional Sample Exercises