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

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| W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William ||  HW2:[http://curtis.ml.cmu.edu/w/courses/images/d/de/10601-13F-assignment_2.pdf Naive Bayes & Voted Perceptron]  Download:[http://curtis.ml.cmu.edu/w/courses/images/c/cc/Assignment2-handout.zip data]  (due Sept. 18th via Autolab)
 
| W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William ||  HW2:[http://curtis.ml.cmu.edu/w/courses/images/d/de/10601-13F-assignment_2.pdf Naive Bayes & Voted Perceptron]  Download:[http://curtis.ml.cmu.edu/w/courses/images/c/cc/Assignment2-handout.zip data]  (due Sept. 18th via Autolab)
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[http://curtis.ml.cmu.edu/w/courses/images/d/d4/Hw3_solution_example.zip example solution]
 
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| M 9/16 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  
 
| M 9/16 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  

Revision as of 16:56, 2 October 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 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 Download:data (due Sept. 18th via Autolab)

example solution

M 9/16 Logistic Regression William
W 9/18 SVMs and Margin Classifiers William HW3: Logistic Regression Download: data (due Sept. 25th via Autolab)
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: Experimentation: Compare classifiers
M 10/7 PAC Learning Eric (William out)
W 10/9 Bias-Variance Decomposition Eric (William out) HW6: 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: Worksheet on Graphical Models
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: