Lect.

Date

Topic

Reading

HW

L1

2/8, Tue.

Registration, Introduction

Chapter 1

L2

2/10, Thu.

ML, Bayesian approach I

Chapter 2, 3, 4

L3

2/15, Tue.

Bayesian approach II

Chapter 2, 3, 4

L4

2/17, Thu.

Decision Theory I

Chapter 2, 3, 4

L5

2/22, Tue.

Non-parametric Methods

Chapter 2

L6

2/24, Thu.

EM

Chapter 8

HW1 out

--

3/1, Tue.

No class(Samiljeol)

--

L7

3/3, Thu.

Graphical Model I

Chapter 8

L8

3/8, Tue.

Graphical Model lI

Chapter 8

L9

3/10, Thu.

Graphical Model IlI

Chapter 8

HW1 due, HW2 out

L10

3/15, Tue.

Regression l

Chapter 3

L11

3/17, Thu.

Regression ll

Chapter 3

L12

3/22, Tue.

Linear Classifiers

Chapter 4

L13

3/24, Thu.

Large-margin Classifier

Chapter 6, 7

HW2 due

--

3/29, Tue.

No Class(Midterm exam week)

--

--

3/31, Thu.

No Class(Midterm exam week)

--

L14

4/5, Tue.

Perceptron & Neural Network

Chapter 4

L15

4/7, Thu.

Kernel Classifiers I

Chapter 6, 7

L16

4/12, Tue.

Kernel Classifiers II

Chapter 6, 7

L17

4/14, Thu.

Model selection I

Chapter 3, 7

L18

4/19, Tue.

Model selection II: Generalization bounds, VC dimensions

Chapter 3, 7

HW3 out

L19

4/21, Thu.

Nearest Neighbor

Chapter 2

L20

4/26, Tue.

Tree

Chapter 8

L21

4/28, Thu.

Boosting l

Chapter 14

L22

5/3, Tue.

Boosting ll

Chapter 14

HW3 due

--

5/5, Thu.

No Class(Children's day)

--

--

5/10, Tue.

No Class(Vesak)

--

L23

5/12, Thu.

Feature Selection

--

HW4 out

L24

5/17, Tue.

Sampling

Chapter 11

L25

5/19, Thu.

Non-parametric Bayesian approach

Chapter 6

--

5/24, Tue.

No Class(Final exam week)

--

--

5/26, Thu.

No Class(Final exam week)

--

HW4 due