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

Teaching team: also see the Google Doc Spreadsheet

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 (due Sept. 13th via BlackBoard)
M 9/9	The Naive Bayes algorithm	William
W 9/11	The Perceptron algorithm	William	HW:Naive Bayes & Voted Perceptron Download:data (due Sept. 13th via Autolab)
M 9/16	The Perceptrons, SVMs, and other Margin Classifiers	William
W 9/18	Logistic Regression	William	HW: Implementation
M 9/23	Linear regression and BackProp	Eric
W 9/25	Neural networks and Deep Belief Networks	Eric	HW: Implementation
M 9/30	K-NN, Decision Trees, and Kernels	William
W 10/2	Comparing Classifiers Experimentally	William	HW: Experimentation
M 10/7	PAC Learning	Eric (William out)
W 10/9	Bias-Variance Decomposition	Eric (William out)	HW: TBA
M 10/14	Ensemble Learning Techniques 1	William
W 10/16	Ensemble Learning Techniques 2	William	Project milestone
M 10/21	Unsupervised Learning: k-Means and Mixtures	Eric
W 10/23	Unsupervised Learning: Dimensionality Reduction	Eric	Project milestone
M 10/28	Semi-Supervised Learning	William
W 10/30	Collaborative Filtering and Matrix Factorization	William	Project milestone
M 11/4	Graphical Models 1	Eric
W 11/6	Graphical Models 2	Eric	HW: TBA
M 11/11	HMMS, Sequences, and Structured Output Prediction	William
W 11/13	Topic Models	Eric (William out)	Project milestone
M 11/18	Topic Models	Eric
W 11/20	Review Session/Special Topics	Eric
M 11/25	Final Exam
W 11/27	No class - Thanksgiving
M 12/2	Markov Decision Processes and Reinforcement Learning	Eric
W 12/4	Scalable Learning and Parallelization	William	Project milestone
Th 12/9			Project due

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