Izhak Shafran
CSEE, OHSU
News Course Information Lectures Textbooks Schedule Links
There will be no final exam. Instead, the course requires a final project of interest to student, chosen in consultation with the instructor. The project requires a written report and a final presentation. In most cases, the data, software toolkit, and key components for the project will be made available. The students will also get an opportunity to present papers related to the topics covered under the syllabus and related to their project.
| Overview | Administrivia, Learning -- Supervised, Unsupervised, Other | |
| Introduction | Bishop Ch.1 | Background: Probability & Statistics, Gaussians, Detection Theory, Information Theory |
| Linear Models for Regression | Bishop Ch.3 | Linear basis function models; bias-variance; Bayesian linear regresson |
| Linear Models for Classification | Bishop Ch.4 | Discriminant functions; Probabilistic Generative Models; Probabilistic Discriminative Models |
| Decision Trees | Mitchell Ch.3 | Decision tree learning algorithm; bias issues |
| Neural Networks | Bishop Ch.5 | Feed-forward networks; Error back propagation; Regularization |
| Large Margin Machines | Bishop Ch.7 | Maximum margin machines; support vector machines; relevance vector machines |
| Perceptron Algorithm | Article | Perceptron algorithm; application to sequence processing (tagging); feature selection |
| Kernel Methods | Bishop Ch.6 | Dual representations; constructing kernels; radial basis functions |
| Feature Selection/Reduction | Bishop Ch.12 | Features transformations; PCA; kernel PCA; ICA; other feature selection strategies |
| Reinforcement Learning | Mitchell Ch.13 | Learning task; MDPs; Q learning more slides |
| Graphical Models | Bishop Ch.8 | Probabilisitic graphical models |
| Lectures | Tue/Thu 1130 - 1300 hrs |
| Venue (West Campus, in-person) | Central 123 |
| Office hours | Central 123, Thu 2:30pm -- 3:30pm with prior email confirmation |