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

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| Thurs 9/18 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  
 
| Thurs 9/18 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  
 
|-  
 
|-  
−
| Mon-Tues 9/22-23 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William || William's also lecturing in Ziv's class on Mon
+
| (Mon +) Tues 9/23 || [[10-601 Perceptrons and Voted Perceptrons|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''
 
| 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
+
| (Mon +) Tues 9/30 ||  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
+
| (Wed +) Thurs 10/1-2 ||  SVMs and Margin Classifiers 2 || Ziv ||  Ziv's also lecturing in his class on Wed
 
|-                                                                                 
 
|-                                                                                 
 
| Tues 10/7 || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]]  || William ||
 
| Tues 10/7 || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]]  || William ||
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| Thus 10/9 || [[10-601 PAC| PAC Learning]] || William ||
 
| Thus 10/9 || [[10-601 PAC| PAC Learning]] || William ||
 
|-                                                                                      
 
|-                                                                                      
−
| Mon-Tues 13-14  || [[10-601 Bias-Variance|Bias-Variance Decomposition]] || William || William's also lecturing in Ziv's class on Mon
+
| (Mon +) Tues 10/14  || [[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
+
| Thurs 10/16 || [[10-601 Ensembles|Ensemble Methods 1]] || William ||  
−
|-    
 
−
| 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      
+
| Tues 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Bhavana or Ziv ||      
 
|-                                                                                      
 
|-                                                                                      
−
| W 10/23  || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| Eric  ||  
+
| Thus 10/23  || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| Bhavana or Ziv ||  
−
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]
 
 
|-                                                                                      
 
|-                                                                                      
−
| M 10/28 || [[10-601 SSL| Semi-Supervised Learning]] || William
+
| Tues 10/28 || Review session || William || ''slides to be posted''
 
|-                                                                                      
 
|-                                                                                      
−
| 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]
+
| Thurs 10/30 || mid-term exam || n/a || ''TBA: room and/or time may be different''
 
|-                                                                                      
 
|-                                                                                      
−
| M 11/4 || [[10-601 GM1| Graphical Models 1]]  || Eric        
+
| Tues 11/4 || [[10-601 GM1| Graphical Models 1]]  || Ziv        
 
|-                                                                                      
 
|-                                                                                      
−
| W 11/6 || [[10-601 GM2| Graphical Models 2]] || Eric  || HW7: [http://curtis.ml.cmu.edu/w/courses/images/6/62/10601-13F-assignment_7.pdf Graphical Models] (due Nov. 13th before class via BlackBoard)
+
| Thurs 11/6 || [[10-601 GM2| Graphical Models 2]] || Ziv  || HW7: [http://curtis.ml.cmu.edu/w/courses/images/6/62/10601-13F-assignment_7.pdf Graphical Models] (due Nov. 13th before class via BlackBoard)
 
|-                                                                                      
 
|-                                                                                      
 
| M 11/11 || [[10-601 Sequences|HMMS, Sequences, and Structured Output Prediction]] || William                                             
 
| M 11/11 || [[10-601 Sequences|HMMS, Sequences, and Structured Output Prediction]] || William                                             

Revision as of 16:24, 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 9/23 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 9/30 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 10/14 Bias-Variance Decomposition William William's also lecturing in Ziv's class on Mon
Thurs 10/16 Ensemble Methods 1 William
Tues 10/21 Unsupervised Learning: k-Means and Mixtures Bhavana or Ziv
Thus 10/23 Unsupervised Learning: Dimensionality Reduction Bhavana or Ziv
Tues 10/28 Review session William slides to be posted
Thurs 10/30 mid-term exam n/a TBA: room and/or time may be different
Tues 11/4 Graphical Models 1 Ziv
Thurs 11/6 Graphical Models 2 Ziv 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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