Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2014"

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| Wed-Thurs 10/1-2 ||  SVMs and Margin Classifiers 2 || Ziv ||  Ziv's also lecturing in his class on Wed
 
| Wed-Thurs 10/1-2 ||  SVMs and Margin Classifiers 2 || Ziv ||  Ziv's also lecturing in his class on Wed
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|-                                                                                
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| M 9/30 || [[10-601 K-NN And Trees|K-NN, Decision Trees, and Rule Learning]] || William ||
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| Tues 10/7 || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]] || William ||
 
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|-                                                                                      
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| W 10/2 || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]]  || William || HW5: [http://curtis.ml.cmu.edu/w/courses/images/5/5a/10601-13F-assignment_5.pdf Compare classifiers] Download: [http://curtis.ml.cmu.edu/w/courses/images/a/ad/Assignment_5-1.mat data1] [http://curtis.ml.cmu.edu/w/courses/images/1/1b/Assignment_5-2.mat data2]  (due Oct. 9th ('''Before lecture''') via Autolab) Example code: [http://curtis.ml.cmu.edu/w/courses/images/3/34/Hw5_sample.tar]
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| Thus 10/9 || [[10-601 PAC| PAC Learning]] || William ||
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|-                                                                                    
 
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| M 10/7 || [[10-601 PAC| PAC Learning]] || Eric ''(William out)''
 
 
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|-                                                                                      
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| W 10/9 || [[10-601 Bias-Variance|Bias-Variance Decomposition]] || Eric ''(William out)'' || HW6:  [http://curtis.ml.cmu.edu/w/courses/images/6/6c/Assignment_6.pdf PAC and VC dimension ]
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| Mon-Tues 13-14 || [[10-601 Bias-Variance|Bias-Variance Decomposition]] || William || William's also lecturing in Ziv's class on Mon
 
|-                                                                                      
 
|-                                                                                      
 
| M 10/14 ||  [[10-601 Ensembles 1|Ensemble Methods 1]] || William
 
| M 10/14 ||  [[10-601 Ensembles 1|Ensemble Methods 1]] || William

Revision as of 16:17, 11 July 2014

This is the syllabus for Machine Learning 10-601 in Fall 2014.

Schedule

Schedule for 10-601 in Fall 2013
Date of lecture Topic Lecturer Assignment/Notes
Tues 9/2 Overview and Intro to Probability William
Thurs 9/4 Classification and K-NN William slides will be updated
Tues 9/9 Decision Trees, and Rule Learning William slides will be updated
Thurs 9/11 The Naive Bayes algorithm William
Tues 9/16 Linear Regression William slides will be updated
Thurs 9/18 Logistic Regression William
Mon-Tues 9/22-23 The Perceptron algorithm William William's also lecturing in Ziv's class on Mon
Thurs 9/25 Neural networks and Deep Belief Networks William slides will be updated
Mon-Tues 9/29-30 SVMs and Margin Classifiers 1 Ziv Ziv's also lecturing in his class on Mon
Wed-Thurs 10/1-2 SVMs and Margin Classifiers 2 Ziv Ziv's also lecturing in his class on Wed
Tues 10/7 Evaluating and Comparing Classifiers Experimentally William
Thus 10/9 PAC Learning William
Mon-Tues 13-14 Bias-Variance Decomposition William William's also lecturing in Ziv's class on Mon
M 10/14 Ensemble Methods 1 William
W 10/16 Ensemble Methods 2 William Project description: [1] 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:

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