Syllabus for Machine Learning 10-601 in Fall 2013

This is the syllabus for Machine Learning 10-601 in Fall 2013.

Prezi Overview of All the Topics in the Course

Link to Prezi Overview

Schedule

TAs and Eric: For now, let's use the Google Doc Spreadsheet to plan the lectures. Later we can migrate to the wiki schedule below - but it's a little hard to swap things around in the wiki format

Schedule for 10-601 in Fall 2013

Date	Topic	Lecturer	Assignment
M 9/2	No class - Labor day
W 9/4	Overview and Intro to Probability	William	HW: worksheet on probabilities
M 9/9	The Naive Bayes algorithm	William
W 9/11	The Perceptron algorithm	William	HW: Implement two learners
M 9/16	The Perceptrons, SVMs, and other Margin Classifiers	William
W 9/18	Logistic Regression	William	HW: Implement two learners
M 9/23
W 9/25
M 9/30
W 10/2
M 10/7	....	Eric (William out)
W 10/9	...	Eric (William out)
M 10/14
W 10/16
M 10/21
W 10/23
M 10/28
W 10/30
M 11/4
W 11/6
M 11/11
W 11/13	...	Eric (William out)
M 11/18
W 11/20
M 11/25
W 11/27	No class - Thanksgiving
M 12/2
W 12/4

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

• 10-601 Introduction to Probability
• 10-601 Naive Bayes
• 10-601 Logistic Regression

Another view of classification:

• 10-601 Introduction to Linear Algebra
• 10-601 Perceptrons and Voted Perceptrons
• 10-601 Voted Perceptrons and Support Vector Machines

Summary:

• 10-601 Wrap-up on Linear Classification