Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"

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| M 2/15 || [[10-601 Ensembles]] || Nina || ||
 
| M 2/15 || [[10-601 Ensembles]] || Nina || ||
 
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| W 2/17 || [[10-601 PAC| Theory 1]] || Nina || ||
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| W 2/17 || [[10-601 PAC| Theory 1]] || Nina || || HW4: Theory
 
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| M 2/22 || [[10-601 PAC 2|Theory 2]] || Nina || ||
 
| M 2/22 || [[10-601 PAC 2|Theory 2]] || Nina || ||
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| M 2/29 || Midterm exam || || ||
 
| M 2/29 || Midterm exam || || ||
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| W 3/2 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Nina || ||
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|-                                                                                    
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| M 3/7 || [[10-601 SSL| Semi-Supervised Learning]] || Nina || ||
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| W 3/9 || [[10-601 Active Learning]] || Nina || ||
 
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| M 9/30 || [[10-601 K-NN And Trees|K-NN, Decision Trees, and Rule Learning]] || William ||
 
| M 9/30 || [[10-601 K-NN And Trees|K-NN, Decision Trees, and Rule Learning]] || 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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| M 10/7 ||  || Eric ''(William out)''
 
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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 ]
 
| 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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|-                                                                                    
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| M 10/14 ||  [[10-601 Ensembles 1|Ensemble Methods 1]] || William
 
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| W 10/16 || [[10-601 Ensembles 2|Ensemble Methods 2]] || William || Project description: [http://www.cs.cmu.edu/~wcohen/10-601/project-proposal/project.pdf] and Project Milestone 1
 
 
|-                                                                                      
 
|-                                                                                      
 
| M 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Eric      
 
| M 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Eric      
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| W 10/23  || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| Eric  ||  
 
| W 10/23  || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| Eric  ||  
 
Project milestone 2: [http://curtis.ml.cmu.edu/w/courses/images/9/9c/10601-13F-milestone_2.pdf Description], [http://curtis.ml.cmu.edu/w/courses/images/8/8f/Team_classifiers.pdf Classifier assignments], and [http://www.cs.cmu.edu/~wcohen/10-601/dataout.zip Datasets for Milestone 2]
 
Project milestone 2: [http://curtis.ml.cmu.edu/w/courses/images/9/9c/10601-13F-milestone_2.pdf Description], [http://curtis.ml.cmu.edu/w/courses/images/8/8f/Team_classifiers.pdf Classifier assignments], and [http://www.cs.cmu.edu/~wcohen/10-601/dataout.zip Datasets for Milestone 2]
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|-                                                                                    
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| M 10/28 || [[10-601 SSL| Semi-Supervised Learning]] || William
 
 
|-                                                                                      
 
|-                                                                                      
 
| W 10/30 || [[10-601 CF|Collaborative Filtering and Matrix Factorization]] || William || Project milestone 3 [https://blackboard.andrew.cmu.edu/bbcswebdav/pid-545079-dt-content-rid-3786031_1/courses/F13-10601/millstone3_update.pdf Milestone3 Handout]
 
| W 10/30 || [[10-601 CF|Collaborative Filtering and Matrix Factorization]] || William || Project milestone 3 [https://blackboard.andrew.cmu.edu/bbcswebdav/pid-545079-dt-content-rid-3786031_1/courses/F13-10601/millstone3_update.pdf Milestone3 Handout]

Revision as of 15:51, 6 January 2016

This is the syllabus for Machine Learning 10-601 in Spring 2016.

Schedule

In progress....

Teaching team: also see the Google Doc Spreadsheet

Schedule for 10-601
Date Main Topic of Lecture Lecturer Assignment TAs
M 1/11 10-601 Course Overview Nina
W 1/13 Intro to Probability William HW1 will be similar to this Will, Han
M 1/18 Martin Luther King Day
W 1/20 The Naive Bayes algorithm William HW2: implementing naive Bayes Travis, Maria
M 1/25 Logistic Regression William
W 1/27 Perceptrons and SVMs William
M 2/1 10-601 Kernels Nina
W 2/3 Linear Regression Nina HW3: implementing logistic regression and kernel perceptrons Tianshu, Will
M 2/8 10-601 Neural Nets 1 Nina
W 2/10 10-601 Neural Nets 2 Nina
M 2/15 10-601 Ensembles Nina
W 2/17 Theory 1 Nina HW4: Theory
M 2/22 Theory 2 Nina
W 2/24 10-601 Midterm Review Nina
M 2/29 Midterm exam
W 3/2 Unsupervised Learning: k-Means and Mixtures Nina
M 3/7 Semi-Supervised Learning Nina
W 3/9 10-601 Active Learning Nina
M 9/30 K-NN, Decision Trees, and Rule Learning William
W 10/9 Bias-Variance Decomposition Eric (William out) HW6: PAC and VC dimension
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

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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