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

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| Thurs 10/30 || mid-term exam || n/a || ''TBA: room and/or time may be different''
 
| Thurs 10/30 || mid-term exam || n/a || ''TBA: room and/or time may be different''
 
|-                                                                                      
 
|-                                                                                      
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| Tues 11/4 || [[10-601 GM1| Graphical Models 1]]  || Ziv      
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| (Mon +) Tues 11/4 || [[10-601 GM1| Graphical Models 1]]  || Ziv ||  Ziv's also lecturing in his class on Mon    
 
|-                                                                                      
 
|-                                                                                      
−
| 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)
+
| (Wed +) Thurs 11/6 || [[10-601 GM2| Graphical Models 2]] || Ziv  || Ziv's also lecturing in his class on Wed
 
|-                                                                                      
 
|-                                                                                      
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| M 11/11 || [[10-601 Sequences|HMMS, Sequences, and Structured Output Prediction]] || William                                           
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| (Mon +) Tues 11/11 || [[10-601 Sequences|HMMS and Sequences]] || Ziv  || Ziv's also lecturing in his class on Mon                                 
 
|-
 
|-
−
| W 11/13 || [[10-601 Topic Models|d-separation, Explaining away, and Topic Models]]|| William || Project milestone 4             
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| (Wed +) Thus 11/13 || [[10-601 Topic Models|Matrix Factorization and Topic Models]]|| William || William's also lecturing in Ziv's class on Wed, ''slides to be updated''           
 
|-
 
|-
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| M 11/18 || [[10-601 Network Models| Network Models]] || Eric
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| (Mon +) Tues 11/18 || [[10-601 Network Models| Network Models]] || William || William's also lecturing in Ziv's class on Mon
 
|-
 
|-
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| W 11/20 || Review Session/Special Topics || Eric
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| (Wed +) Thurs 11/20 || Semi-supervised learning || William || William's also lecturing in Ziv's class on Wed
 
|-  
 
|-  
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| M 11/25 || [[10-601 Exam|Not-quite-final Exam]] ||  
+
| (Mon +) Tues 11/25 || [[10-601 Big Data|Scalable Learning and Parallelization]] || William || William's also lecturing in Ziv's class on Mon
−
[http://curtis.ml.cmu.edu/w/courses/images/1/13/Final_exam.pdf Exam][http://curtis.ml.cmu.edu/w/courses/images/f/fa/Final_exam_solutions.pdf Solutions]
 
 
|-
 
|-
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| W 11/27 || ''No class - Thanksgiving'' ||  
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| Thurs 11/27 || ''No class - Thanksgiving'' ||  
 
|-  
 
|-  
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| M 12/2 || [[10-601 Markov Decision Processes and Reinforcement Learning| Markov Decision Processes and Reinforcement Learning]] || Eric
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| (Mon +) Tues 12/2 || Learning and NLP || William ||  
 
|-
 
|-
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| W 12/4 || [[10-601 Big Data|Scalable Learning and Parallelization]] || William  || [http://www.cs.cmu.edu/~wcohen/10-601/project-proposal/milestones5-6-final.pdf Milestones 5-6 Description]||  
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| (Wed +) Thurs 12/4 || Learning and Biology || William  ||  
 
|-  
 
|-  
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| Mon 12/9 ||          ||      || Milestone 5 due
 
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|-
 
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| Tue 12/10 ||          ||      || Milestone 6 (writeup) due
 
 
|}
 
|}
  

Revision as of 16:35, 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, Ensemble Methods 2 William slides to be updated
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
(Mon +) Tues 11/4 Graphical Models 1 Ziv Ziv's also lecturing in his class on Mon
(Wed +) Thurs 11/6 Graphical Models 2 Ziv Ziv's also lecturing in his class on Wed
(Mon +) Tues 11/11 HMMS and Sequences Ziv Ziv's also lecturing in his class on Mon
(Wed +) Thus 11/13 Matrix Factorization and Topic Models William William's also lecturing in Ziv's class on Wed, slides to be updated
(Mon +) Tues 11/18 Network Models William William's also lecturing in Ziv's class on Mon
(Wed +) Thurs 11/20 Semi-supervised learning William William's also lecturing in Ziv's class on Wed
(Mon +) Tues 11/25 Scalable Learning and Parallelization William William's also lecturing in Ziv's class on Mon
Thurs 11/27 No class - Thanksgiving
(Mon +) Tues 12/2 Learning and NLP William
(Wed +) Thurs 12/4 Learning and Biology William

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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