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 Material: COMP4702/COMP7703
The University of Queensland
School of Information Technology and Electrical Engineering
Semester 1, 2008

COMP4702/COMP7703 - Machine Learning

Course Material

Lecture Notes

Notes are listed here in the order that we will cover them in the course. These slides are based on those provided by Alpaydin (the author of the text), with modifications made where possible.

Textbooks

  • Course text: Introduction to Machine Learning. Ethem Alpaydin, The MIT Press, October 2004. Book Website (including errata)
  • Reference texts:
    • The text for the AI course (COMP3702) is a useful reference - Russell S. and Norvig P., Artificial Intelligence: A modern approach, 2nd ed., 2003. Prentice Hall.
    • R. Duda, P. Hart and D. Stork. Pattern Classification, Second edition. Wiley, 2001.
    • [Bis] Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006.
    • [HTF] T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. 2001.
    • D. Hand, H. Mannila and P. Smyth, Principles of Data Mining, MIT Press, 2001.

Pracs

Assignments

The assignments will be comprised of some of the questions on the pracs. If you complete each prac, producing your assignment will be quite easy. (NB: in the assignment question a.b refers to question b from Prac a). Assignments should be submitted in hardcopy to the submission box in level 1, GP South, or electronically via submit.itee.uq.edu.au.

  • Assignment 1: Questions 1.4, 2.4, 2.5, 3.1, 3.3, 4.1, 4.3, 4.4. Due Thursday 5pm, 9/4/09.
  • Assignment 2: Questions 5.1, 5.2, 6.2, 6.3, 7.3, 7.4, 8.6, 8.7, 9.5, 9.6. Due 5pm Friday, 5/6/09.
Assignment results (at 19/06/08) available here.

Exams

The 2008, 2007 and 2006 exams are available from the library web.

The 2005 exam is also available. Note however that the course content has changed extent, hence some of the 2005 exam is irrelevant for you. In particular, you should ignore questions: 3(a), most of(b), (d); 6. Some of Q1 is a little out of context also. Please ask the lecturer if you need more clarification about the 2005 exam questions.

Study Guide/notes

Note that we do NOT cover the following material in lectures (i.e. it is not examinable/assessable):
  • Chapter 7: 7.5, mixture of mixtures model (under 7.6).
  • Chapter 10: 10.8, the formulation of the optimization problems for SVMs in 10.9, 10.9.4.
  • Chapter 11: 11.9-11.11 only very briefly covered, 11.12 not covered.
  • Chapter 12: not covered at all (we didn't get time for this).
  • Chapter 13: 13.8-13.10. Also, the derivations/inner workings of the Baum-Welch, Viterbi and Forward-backward algorithms do not need to be remembered in detail - just the general principles of how they work.
  • Chapter 14: 14.5 and 14.6 covered only briefly, 14.7, 14.8.
  • Chapter 15: 15.3, 15.7, 15.8.

Reference Material


Last modified: 19/06/09