Machine Learning

Computer Science CSCI-B555

Spring 2015

▪ **How to
prepare for the class**:
see here

▪ Syllabus

▪
**Midterm
Exam:** Week 9, March 10th, in class.

▪
**Final exam** is scheduled for Thursday May 7th, 2:45pm-4:45pm, in LH008.
Official
IU
exam schedule.

▪
**Office Hours AFTER the final exam** will be on Friday May 8th, 4pm-5:30pm, in
LH301F.

▪
**Instructor's lecture notes**: 1, 2,
3, 4, 5,
5 (updated 04/03)
6, 7, 8,
9, 10, 11.

▪
**Instructor's slides**: 1, 2,
3, 4 (from a different course),
5 (updated on 5/1).

▪
**Students' lecture notes from 2010**: 1, please be careful, some have
errors.

**Week 1**:
January 12-16, 2015

**Topics**

Class introduction

Review of probability theory

**Reading material**

Textbook: Introduction (Chapter 1)

**Week
2**:
January 19-23, 2015

**Topics**

Random variables

Introduction to parameter estimation

**Reading material**

Textbook: Introduction (Chapter 1)

**Week
3**: January 26-30, 2015

**Topics**

Parameter estimation

Maximum a posteriori (MAP)

Maximum likelihood (ML)

Bayesian principles

**Week
4**: February 2-6, 2015

**Topics**

Expectation-maximization (EM) algorithm

**Reading material**

Textbook: Mixture models and EM (Chapter 9)

**Code from
class**

Matlab code for the EM algorithm.

**Week
5**: February 9-13, 2015

**Topics**

Basics of classification and regression

Ordinary least-squares (OLS) regression

**Reading material**

Textbook: Introduction (Chapter 1)

Textbook: Linear models for regression (Chapter 3)

**Week
6**: February 16-20, 2015

**Topics**

Algebraic view of linear regression

Non-linear regression using OLS regression

Regularization

**Reading material**

Textbook: Linear models for regression (Chapter 3)

**Code from
class**

Matlab code for the linear regression.

**Week
7**: February 23-27, 2015

**Topics**

Bayesian linear regression

Logistic regression

**Reading material**

Textbook: Linear models for classification (Chapter 4)

**Week
8**: March 2-6, 2015

**Topics**

Perceptron

The Pocket algorithm

**Reading material**

Textbook: Linear models for classification (Chapter 4)

The Pocket Algorithm can be found from here

**Code from
class**

Matlab code for the logistic regression.

Matlab code for the pocket algorithm.

**Week
9**: March 9-13, 2015

**Midterm Exam,
Tuesday, in class.**

**Week 10**:
March 16-20, 2015

**Spring Break!**

**Week 11**:
March 23-27, 2015

**Topics**

Naive Bayes classifiers

**Reading material**

Textbook: Linear models for classification (Chapter 4)

Tom Mitchel's preliminary book chapter on Naive Bayes classifiers is here.

**Week 12**:
March 30-April 3, 2015

**Topics**

Data-preprocessing

Performance estimation

**Reading material**

Jiawei Han's slides are here.

Tom Mitchel's book chapter on accuracy estimation, posted in Oncourse.

(Homework Assignment #3) (HW3 data)

**Week 13**:
April 6-April 10, 2015

**Topics**

Classification and regression trees

**Reading material**

Tom Mitchel's book chapter on decision trees, posted in Oncourse.

**Week 14**:
April 13-April 17, 2015

**Topics**

Classification and regression trees

Neural networks

**Reading material**

Textbook: Neural networks (Chapter 5)

Tom Mitchel's book chapter on decision trees, posted in Oncourse.

**Week 15**:
April 20-April 24, 2015

**Topics**

Neural networks

**Reading material**

Textbook: Neural networks (Chapter 5)

RPROP paper available here.

Papers by Breiman and Freund & Shapire available here: 1, 2, 3

**Code from
class**

Matlab code for the committee machines.

**Week 16**:
April 27-May 1, 2015

**Topics**

Committee machines

Support vector machines

**Reading material**

Textbook: Combining Models (Chapter 14)

Textbook: Sparse kernel machines (Chapter 7)

**Week 17**:
May 4-8, 2015

**Final Exam,
Thursday, 2:45pm **

Last updated: 05/01/2015 04:41 PM