Week |
Topics |
Readings |
1 |
Overview of Machine Learning |
Texts: Mitchell, Chapter 1 Nilsson, Chapter 1, Chapter 2 |
|
Concept Learning and the General-to-Specific Ordering |
Texts: Mitchell, Chapter 2 Nilsson, Chapter 3 Papers: Ali, Brunk & Pazzani, 1994 |
3 |
Decision Tree Learning |
Texts: Mitchell, Chapter3 Nilsson, Chapter 6 Papers: Friedman, Kohavi & Yun, 1996 |
4 |
Artificial Neural Networks |
Texts: Mitchell, Chapter 4 Nilsson, Chapter 4 Papers: Fahlman & Lebiere, 1990 |
5 |
Experimental Evaluation of Learning Algorithms |
Texts: Mitchell, Chapter 5 Papers: Geman, Bienenstock & Doursat (1992). Neural Networs and the bias/variance dilemma. Neural Computation 4, 1-58. (Available from Killiam) |
6 |
Bayesian Learning |
Texts: Mitchell, Chapter 6 Nilsson, Chapter 5 Papers: Joachims, 1996 |
7 |
Instance-Based Learning |
Texts: Mitchell, Chapter 8 Papers: Kasif et al., 1998 |
8 |
Computational Learning Theory |
Texts: Mitchell, Chapter 7 Nilsson, Chapter 8 Papers: Kearns et al. 1991
|
9 |
Rule Learning/Inductive Logic Programming |
Texts: Mitchell, Chapter 10 Nilsson, Chapter 7 Papers: Bratko & Muggleton, 1995 |
10 |
Unsupervised Learning |
Texts: Nilsson, Chapter 9 Papers: |
11 |
Genertic Algorithms |
Texts: Mitchell, Chapter 9 Papers: Koza et al., 1998 |
12 |
Combining Classifiers, Mixture Models |
Papers: Breiman, 1996 Jacobs et al., 1991 |
13 |
Projects Presentation |
(Note: Certain topics currently listed in the syllabus may be replaced by other topics such as Reinforcement Learning, Genetic Algorithms, etc.)