CS 760 - Machine Learning
General Course Information
Course Overview and Requirements
The intent of this course is to present a broad introduction to machine
learning, including discussions of each of the major approaches currently
being investigated.
Class lectures will discuss general issues in machine learning, as well as
present established algorithms. One secondary goal is to compare and contrast
the various approaches, determining under which conditions each is most
appropriate. Another is to relate these algorithms to human learning
processes.
The work in the course will consist of four homework assignments (about
one every two weeks during the first part of the class),
one exam (very late in the semester), and a course project.
Your solutions will be partially automatically graded, so they
must be written to run in Java on the instructional computers.
The homework assignments will generally be programming assignments that
involve experimenting with machine learning algorithms and experimental methodology.
Lab reports insightfully analyzing your experimental results will be required.
Occasionally there may also be "paper-and-pencil" homework problems.
The final project can be a more ambitious experiment or enhancement involving
algorithms used in the homeworks or the implementation of an additional algorithm.
In either case, the implementation should be accompanied by a short paper
(7-10 pages with a large font) describing the project. Projects not involving
programming are also possible.
Homeworks will count for 35% of the grade, the "midterm" exam
for 40%, the project 20%, and quality class participation 5%.
The course will be graded on the conventional (A-F) system.
Lecture Notes
Powerpoint files containing each week's lectures
will be available as the course progresses.
(Access is limited to University of Wisconsin sites.)
Reading Assignments
- Assigned December 6, 2006:
- Read Chapter 11 and 12 of Mitchell's textbook.
- Assigned December 1, 2006:
- Read Chapter 10 of Mitchell's textbook.
- Assigned November 20, 2006:
- Read Chapter 7 of Mitchell's textbook.
- Assigned November 8, 2006:
- Read the SVM tutorial
from ICML-2001 given by Nello Cristianini.
- Assigned November 1, 2006:
- Read Preface, Chapter 1 (just skim Section 1.2), and Sections 6.4, 6.5, and 8.3.2 of
Sutton and Barto's on-line RL textbook.
- Assigned October 23, 2006:
- Read Chapter 13 of Mitchell's textbook.
- Assigned October 11, 2006:
- Read Chapter 4 of Mitchell's textbook.
- Assigned September 29, 2006:
- Pages 97-105 of
'Machine Learning Research: Four Current Directions'
by T. Dietterich in the AI Magazine.
- Assigned September 22, 2006:
- Read Chapter 3 of Mitchell's textbook
and
'Building the Case Against Accuracy Estimation for
Comparing Induction Algorithms' by F. Provost,
T. Fawcett, and R. Kohavi, Proc. ICML-98, Madison, pp. 445-453.
- Assigned September 13, 2006:
- Read Sections 2.1-2.3 and Chapter 5 of Mitchell's textbook.
- Assigned September 6, 2006:
- Read Preface, Chapter 1, Sections 6.1-6.2,
and Sections 8.1-8.2 of Mitchell's textbook. Also read
the on-line draft chapter Mitchell created on Naive Bayes
and Logistic Regression
Homework Assignments
-
Homework 4: Reinforcement Learning
Due Monday, 11/20/06
-
Homework 3: Training Perceptrons
Due Monday, 11/06/06
-
Homework 2: Inducing, Pruning, and Boosting Decision Trees
Due Wednesday, 10/18/06 (changed to Friday 10/20/06 since no class on that Weds)
-
Homework 1: Experimental Methodology, Feature Selection, k-NN, and Naive Bayes
Due Monday, 10/2/06
-
Homework 0: Creating Your Personal Dataset
Due Friday, 9/15/06
- Late policy on HWs:
- HWs are due at 4 pm. If you don't turn
them in at the end of class, then put them in the TA's mailbox
(5th floor of CS).
- Each student will have FIVE "free" late days for use
over the semester. Once these are exhausted, there will be
a penalty of 10 points per day (measured 4pm-to-4pm; weekends
and university holidays are free).
"Days" are calendar days, not class days.
- To make the TA's job tractable, no HWs will be accepted more than
one week late.
- Academic Misconduct
All examinations, programming assignments, and written homeworks must
be done individually. Cheating and
plagiarism will be dealt with in accordance with University
procedures (see the
Academic Misconduct Guide for Students).
Hence, for example, code for programming assignments must not
be developed in groups, nor should code be shared. You are
encouraged to discuss with your peers, the TAs or
the instructor ideas, approaches and techniques broadly, but not at a level
of detail where specific implementation issues are described by anyone.
If you have any questions on this, please ask the instructor before you act.
Previous Exams (postscript until 1999; MS Word after)
Some ML-Related Links
Related Local Links
This page was created by
shavlik@cs.wisc.edu
Computer Sciences Department
College of Letters and Science
University of Wisconsin - Madison
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