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

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Schedule

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	Logistic Regression	William
W 9/18	Evaluating and comparing classifiers	William	HW: Implement logistic regression
M 9/23	Neural networks - 1	Eric
W 9/25	Neural networks - 2	Eric	Project plan 1: Deep Networks for Images
M 9/30	K-nearest neighbor classifiers	Eric
W 10/2	Decision trees	Eric	HW: Compare some classifiers
M 10/7	Linear regression	William
W 10/9	PAC-learning and learning theory	William	HW: worksheet on theory
M 10/14	Bias-variance and linear regression	William
W 10/16	Ensembles and boosting	William	Project plan 2: Robust learners
M 10/21	K-means and Mixture models	Eric
W 10/23	Dimensionality Reduction	Eric	HW: Unsupervised learning
M 10/28	Graph-based semi-supervised learning	William
W 10/30	Modeling distributions with Bayes Nets and Markov Fields - 1	Eric	HW: worksheet on Bayes nets
M 11/4	Modeling distributions with Bayes Nets and Markov Fields - 2	Eric
W 11/6	Topic models - 1	Eric
M 11/11	Topic models - 2	Eric
W 11/13	HMMs	Eric
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