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Lecture Notes

LEC # LECTURE TOPIC OTHER MATERIALS
1 Introduction (PDF)
2 Linear Regression (PDF)
3 Additive Models, Maximum Likelihood (PDF)
4 Active Learning (PDF)
5 Classification (PDF)
6 Logistic Regression, Regularization (PDF) Additional Notes on Regularization (PDF)
7 Regularization, Support Vector Machines (PDF) Tutorial on Lagrange Multipliers (PDF)
8 Support Vector Machines, Text Classification (PDF)
9 Feature Selection, Combination of Methods (PDF)
10 Boosting, Complexity (PDF)
11 Structural Risk Minimization, Description Length
(+ Midterm Discussion) (PDF)
MIDTERM EXAM
12 Mixture Models, EM (PDF)
13 EM, Regularization, Conditional Mixtures (PDF)
14 Non-Parametric Density Estimation, Clustering (PDF)
15 Clustering, Markov Models (PDF)
16 Markov and Hidden Markov Models (PDF)
17 Hidden Markov Models (PDF)
18 Viterbi, Graphical Models (PDF)
19 Bayesian Networks (PDF)
20 Medical Diagnosis Example, Influence Diagrams (PDF)
21 Influence Diagrams, Exact Inference (PDF)
22 Belief Propagation (PDF)
23 Learning Graphical Models (Guest Lecture) (PDF)
FINAL EXAM