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

Textbook: Introduction (Chapter 1)

Week 2: January 19-23, 2015

Topics

Random variables

Introduction to parameter estimation

Textbook: Introduction (Chapter 1)

(Homework Assignment #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

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

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

Textbook: Linear models for regression (Chapter 3)

Code from class

Matlab code for the linear regression.

(Homework Assignment #2)

Week 7: February 23-27, 2015

Topics

Bayesian linear regression

Logistic regression

Textbook: Linear models for classification (Chapter 4)

Week 8: March 2-6, 2015

Topics

Perceptron

The Pocket algorithm

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.

(Class Project)

Week 10: March 16-20, 2015

Spring Break!

Week 11: March 23-27, 2015

Topics

Naive Bayes classifiers

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

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

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

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

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.

(Homework Assignment #4)

Week 16: April 27-May 1, 2015

Topics

Committee machines

Support vector machines

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