COSC 388: Machine Learning

Fall 2006

Schedule

Primary Text:

Week 
Topic 
Chapters 
     
Week 1  Introduction: What is machine learning?
1
Week 2  Introduction: Areas, Brief History, Paradigms
1
Week 3  Concept learning as search: Version spaces
2
Week 4  Instance-based learning, k-NN
8.1, 8.2
Week 5  Bayesian learning: probability, Bayes' Theorem, naive Bayes
6.1, 6.9
Week 6  Bayesian learning: Bayesian networks
6.11, 6.12
Week 7  Evaluation, Experimental Design, Hypothesis Testing
5.1-5.7
Week 8  Decision Trees: ID3, Decision Stumps
3.1-3.6
Week 9  Decision Trees: C4.5, Midterm Exam
3.7-3.8
Week 10  Rule Learning: AQ, OneR
10.1-10.3
Week 11 Rule Learning: C4.5rules
5.4
Week 12 Neural Networks, Perceptron
4.1-4.4
Week 13 Neural Networks, Back-propagation
4.5-4.7
Week 14 Genetic Algorithms, Genetic Programming
9.1-9.5
Week 15 Reinforcement Learning, Q-learning, Temporal difference learning
13.1-13.7
     
12/7 (out), 12/18 (in)  Final Exam
 


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