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

Schedule for 10-601 in Fall 2013

Date	Topic	Lecturer	Due assignment	New assignment
9/2	No class - Labor day
9/4	Overview and Intro to Probability	William
9/9	The Naive Bayes algorithm	William
9/11	Logistic Regression	William
9/16	The Perceptron algorithm	William
9/18	Evaluating and comparing classifiers	William
9/23	Neural networks	Eric
9/25	K-nearest neighbor classifiers	Eric
9/30	Decision trees	Eric
10/2	PAC-learning and learning theory	William
10/7	Bias-variance and linear regression	William
10/9	K-means and Mixture models	William
10/14	Dimensionality Reduction	Eric
10/16	Modeling distributions with Bayes Nets and Markov Fields	Eric
10/21	Learning with graphical models	Eric
10/23	Topic models	William
10/28	HMMs	Eric
10/30
11/4
11/6
11/11
11/13
11/18
11/20
11/25
11/27	Thanksgiving - class cancelled
12/2
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