Chapter

Topic

Matlab Code and Examples

Class dates

2

Linear Discriminants: Perceptron, Linear Separability, Linear Regression

Tatyana Perceptron and Linear Regression

Aug 31, Sep 2, 7

4

Radial Basis Functions: Weight Space, RBF Network, Curse of Dimensionality, Interpolation and Basis Functions, Splines

Xueli RBF Network and Splines

Sep 9, 14, 16

5

Support Vector Machines: Optimal Separation, Kernels

Ana SVM

Sep 21

6

Learning with Trees: Decision Trees, Entropy, Classification and Regression Trees

Avirup Decision Trees

Sep 23, 28, 30

7

Decision by Committee: Ensemble Learning: Boosting, Bagging

Erkang Bagging and Boosting

Oct 5

8

Probability and Learning: Bayes Classifier, EM and GMM, Nearest Neighbor Methods, Distance Measures

Kristiyan Bayes and KNN
Tianyang EM and kdTree

Oct 7, 12, 14, 19

9

Unsupervised Learning: k-Means, Vector Quantization, Self-Organizing Maps

Nemanja k-Means and SOM

Oct 21, 26

10

Dimensionality Reduction:
Linear Discriminant Analysis (LDA), PCA,
Kernel PCA, Multi-Dimensional Scaling

Review: Eigen Decomposition and SVD

Tutorial1, Tutorial2

Po-Shen LDA
Liang Eigenfaces
Qun Kernel PCA and MDS

Oct 28, Nov 2, 4, 9

11

Optimization and Search: Gradient Descent, Newton Direction;
Search: Exhaustive, Greedy, Hill Climbing, Simulated Annealing

Chengliang  Gradient and Search

Nov 16

14

Markov Chain Monte Carlo Methods: Sampling, Proposal Distribution, MCMC, Metroplois-Hastings Algorithm, Gibbs Sampling, Markov Chains

Sampling1, Sampling2

Shoumik Rejection and Importance Sampling

Nan MH and Gibbs Sampling

Nov 18, 23, 30, Dec 2

15

Graphical Models: Bayesian Network, Inference with Gibbs Sampling, MRF, HMM, Kalman Filter, Particle Filter

Dec 7

Review