CSG220 Lecture Slides (pdf format)

Introduction to Machine Learning (1/11)
Decision Tree Induction (1/11)
Version Space Learning (1/18)
Artificial Neural Networks (1/18)
Probabilistic and Bayesian Learning (1/25)
Maximum Likelihood vs. Bayesian Parameter Estimation (2/1)
Linear and Nonlinear Regression and Classification (2/1)
Instance-Based Classification (2/8)
Instance-Based Regression (2/8)
Cross-Validation for Detecting and Preventing Overfitting (2/8)
PAC Learning (2/15)
VC Dimension (2/15)
Support Vector Machines (2/22)
Ensemble Learning (3/1)
Reinforcement Learning and Markov Decision Processes (3/15)
Bayes Networks (3/22)
Learning Bayes Networks (3/29)
Gaussian Mixture Models (3/29)
K-Means Clustering (3/29)
Hidden Markov Models (4/5)
Genetic Algorithms and Genetic Programming (4/19)