Date

Lecture

Reading Materials

1/22/07

Week 1

Introduction

I2ml- chapter 1

1/29/07

Week 2

Supervised Learning Theory:

Hypothesis spaces, VC-dim, Regression, variance-bias trade off, training and validation, error metrics.

I2ml – chapter 2

ML – 7.4.2, 7.4.3

2/5/07

Week 3

Instant-based learning

Decision Trees

ML – 8.1, 8.2, 8.5

ML – chapter 3

2/12/07

Week 4

Bayesian Decision Theory, Bayesian Networks.

I2ml – chapter 3

2/19/07

Week 5

Artificial Neural Networks: Perceptron, Perceptron learning rule, delta rule

 I2ml – chapter 11

2/26/07

Week 6

Artificial Neural Networks – Cont: Multilayered Perceptron, Backpropagation, Structured NN, and Dimensionality Reduction using NN.

I2ml – chapter 11

3/5/07

Week 7

Bayesian Networks -

3/19/07

Week 8

Bayesian Decision theory, parametric methods: Parameter Estimation, MLE, Bayes Estimator, Bias and Variance, Parametric Classification, Multivariate Data

I2ml - 4.1->4.5, 5.1 -> 5.7

3/26/07

Week 9

Reinforcement Learning

 I2ml chapter 16

Linear Discrimination: Generalized linear models and kernel functions, kernel trick.

4/2/07

Week 10

Linear Discrimination: Support Vector Machines

Chapter 10

4/9/07

Week 11

Density Estimation and Clustering

Density Estimation: Nonparametric density estimation, Mixture Models

Unsupervised Learning – Clustering: K-means, hierarchical clustering, mean shift, graph spectral clustering

I2ml Chapter 7

4/16/07

Week 12

4/23/07

Week 13

Dimensionality Reduction: Principle Component Analysis (PCA), SVD, Factor Analysis, MDS, Linear Discriminant Analysis (LDA), Bilinear Models. 

Nonlinear dimensionality Reduction:  Manifold Learning, Local Linear Embedding (LLE), Isomap.

I2ml Chapter 6

4/30/07

Week 14

Structured Models – HMM - 

I2ml Chapter 13

HMM tutorial