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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 30% of the grade, the "midterm" exam for 40%, the project 25%, and quality class participation 5%. The course will be graded on the conventional (A-F) system.

Lecture Notes

Powerpoint files containing each lecture will be available as the course progresses.
(Access is limited to University of Wisconsin sites.)

Reading Assignments

Assigned April 28, 2010:
Read Chapter 10 of Mitchell's textbook.

Read Chapters 1 and 2, plus Sections 4.1 and 4.2 of Markov Logic: An Interface Layer for Articial Intelligence, by Domingos and Lowd, in the Morgan-Claypool Series or read Markov Logic Networks, Richardson & Domingos, MLj:62, 2006.

Assigned April 7, 2010:
Read Chapter 7 of Mitchell's textbook and Chapter 2 of Zhu and Goldberg's book on semi-supervised learning in the Morgan-Claypool Series.

Assigned March 17, 2010:
Read Chapter 13 of Mitchell's textbook.
Read Preface, Chapter 1 (just skim Section 1.2), and Sections 6.4 and 6.5 of Sutton and Barto's on-line RL textbook.

Assigned March 8, 2010:
Read Chapter 4 of Mitchell's textbook and the SVM tutorial from ICML-2001 given by Nello Cristianini.
(You can find other SVM tutorials at; the one by Burgess is a good one to read if you wish additional details.)

Assigned February 24, 2010:
Pages 97-105 of 'Machine Learning Research: Four Current Directions' by T. Dietterich in the AI Magazine.

Random Forests, L. Breiman, Machine Learning, 45, pp. 5-32, 2001 (won't be on the exam, beyond what is covered in the lecture notes)

Model Selection: Beyond the Bayesian/Frequentist Divide , I. Guyon, A. Saffari, G. Dror, and G. Cawley, JMLR 11, pp. 61-87, 2010.

Assigned February 15, 2010:
Read Chapter 3 of Mitchell's textbook

Assigned February 10, 2010:
Read 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.

Read Sections 6.7-6.11 (skim 6.11.5) and 6.13 of Mitchell's textbook.

Assigned January 27, 2010:
Read Sections 2.1-2.3 and Chapter 5 of Mitchell's textbook.

Assigned January 20, 2010:
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

Previous Exams

Postscript until 1999 (converted from postscript to pdf using unix' ps2pdf); MS Word after.

Some ML-Related Links

Related Local Links

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Computer Sciences Department
College of Letters and Science
University of Wisconsin - Madison


5355a Computer Sciences and Statistics ~ 1210 West Dayton Street, Madison, WI 53706 ~ voice: 608-262-1204 ~ fax: 608-262-9777