Date

Area

Topic

Lecture Notes

Readings

Tu - Jan. 10

Introduction

Lecture-1

ML: Ch. 1

Th - Jan. 12

Regression

Linear Classification Models

Probability Theory

Lecture-2

　PRML: Ch. 1

Tu - Jan. 17

Linear Regression

Lecture-3

　PRML: Ch. 1 and 3

Th - Jan. 19

Linear Regression (cont'd)

Tu - Jan. 24

Linear Regression (cont'd)

Th - Jan. 26

Linear Regression (cont'd)

Tu - Jan. 30

Instance-based Learning

& Kernel Density Function

Lecture-4

ML: Ch. 8

Leave-one-out-log-liklihood

Tutorial for KD-Tree

PRML: Ch 2.5

Th - Feb. 2

Instance-based Learning

& Kernel Density Function (cont'd)

Tu - Feb. 7

Generative Model

Lecture-5

EoSL: Ch 4

PRML: Ch 4.2

Th - Feb. 9

Nonlinear Classification Models

Generative Model (cont'd)

Tu - Feb. 14

Logistic Regression

Lecture-6

PRML: Ch 4.3

EoSL: Ch. 4

Th - Feb. 16

Maximum Entropy Model

Lecture-7

Tutorial by Adam Berger

MaxEnt for Language Modeling

MaxEnt for Natural Language Processing

Tu - Feb. 21

Maximum Entropy Model(cont'd)

Th - Feb. 23

Support Vector Machine

Lecture-8

SVM Tutorial by Burges

Tu - Feb. 28

Support Vector Machine (cont'd)

Th - Mar. 1

Middle Term Exam

Tu - Mar. 6

No Class

Th - Mar. 8

No Class

Tu - Mar. 13

Kernel Methods

Diffusion Kernel

 Kernel Learning

Th - Mar. 15

Kernel Methods (cont'd)

Tu - Mar. 20

Bayesian Learning

Online Learning

Lecture-9

PRML: 4.1

"Prediction, learning, and games", by Nicolò Cesa-Bianchi and Gábor Lugosi, Ch. 11

Th - Mar. 22

Online Learning (cont'd)

Tu - Mar. 27

Bagging

Lecture-10

EoSL: Ch 7

ML: Ch 6

Th - Mar. 29

Boosting

Lecture-11

Tu - Apr. 3

Hidden Markov Model

Lecture-12

Th - Apr. 5

Hidden Markov Model(cont'd)

Project (Phase 2): full training data and test examples are available. You are requested to submit your predictions before 11:59pm Apr. 18 (Wednesday)

Tu - Apr. 10

Unsupervised/Semi-supervised Learing

Clustering

Lecture-13

Improved K-means

 EM and Gaussian Mixture Model

Th - Apr. 12

Clustering (cont'd)

Tu - Apr. 17

Expectation Maximization & Bound Optimization

Lecture-14

Project (submission): submit your prediction before 11:59pm Apr. 18 (Wednesday)

Th - Apr. 19

Expectation Maximization & Bound Optimization (cont'd)

Tu - Apr. 24

Project Presentation

Th - Apr. 26