Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"

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| M 11/11 || HMMS, Sequences, and Structured Output Prediction || William                                             
 
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Revision as of 10:40, 27 August 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: Implementation
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:
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:

Another view of classification:

Summary:

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