Syllabus for Machine Learning 10-601 in Fall 2013

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

Contents

Prezi Overview of All the Topics in the Course

Link to Prezi Overview

Schedule

Note: the days for each lecture will be adjusted - these are the dates from 2013.

Teaching team: also see the Google Doc Spreadsheet

Schedule for 10-601 in Fall 2013
Date of lecture Topic Lecturer Assignment
M 9/2 No class - Labor day
W 9/4 Overview and Intro to Probability William HW1: worksheet on probabilities (due Sept. 13th via BlackBoard)
M 9/9 The Naive Bayes algorithm William
W 9/11 The Perceptron algorithm William HW2:Naive Bayes & Voted Perceptron (due Sept. 18th via Autolab)
M 9/16 Logistic Regression William
W 9/18 SVMs and Margin Classifiers William HW3: Logistic Regression Download: data (due Sept. 25th via Autolab) example solution
M 9/23 Linear Regression Eric
W 9/25 Neural networks and Deep Belief Networks Eric HW4: Linear Regression Download: data (due Oct. 2nd (Before lecture) via Autolab)
M 9/30 K-NN, Decision Trees, and Rule Learning William
W 10/2 Evaluating and Comparing Classifiers Experimentally William HW5: Compare classifiers Download: data1 data2 (due Oct. 9th (Before lecture) via Autolab) Example code: [1]
M 10/7 PAC Learning Eric (William out)
W 10/9 Bias-Variance Decomposition Eric (William out) HW6: PAC and VC dimension
M 10/14 Ensemble Methods 1 William
W 10/16 Ensemble Methods 2 William Project description: [2] and Project Milestone 1
M 10/21 Unsupervised Learning: k-Means and Mixtures Eric
W 10/23 Unsupervised Learning: Dimensionality Reduction Eric

Project milestone 2: Description, Classifier assignments, and Datasets for Milestone 2

M 10/28 Semi-Supervised Learning William
W 10/30 Collaborative Filtering and Matrix Factorization William Project milestone 3 Milestone3 Handout
M 11/4 Graphical Models 1 Eric
W 11/6 Graphical Models 2 Eric HW7: Graphical Models (due Nov. 13th before class via BlackBoard)
M 11/11 HMMS, Sequences, and Structured Output Prediction William
W 11/13 d-separation, Explaining away, and Topic Models William Project milestone 4
M 11/18 Network Models Eric
W 11/20 Review Session/Special Topics Eric
M 11/25 Not-quite-final Exam

ExamSolutions

W 11/27 No class - Thanksgiving
M 12/2 Markov Decision Processes and Reinforcement Learning Eric
W 12/4 Scalable Learning and Parallelization William Milestones 5-6 Description
Mon 12/9 Milestone 5 due
Tue 12/10 Milestone 6 (writeup) due

To other instructors: if you'd like to use any of the materials found here, you're absolutely welcome to do so, but please acknowledge their ultimate source somewhere.

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

Another view of classification:

Summary: