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

From Cohen Courses
Jump to navigationJump to search
 
(125 intermediate revisions by 6 users not shown)
Line 3: Line 3:
 
=== Schedule ===
 
=== Schedule ===
  
−
In ro
+
''Teaching team '''only''': also see the  [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt4wbXL8oVXkSnh_igVcq_gwhErfWo/edit#gid=0 Google Doc Spreadsheet]''.  Students should not try and decipher the scribbles and planning notes on this gdoc - use the schedule below. 
−
 
+
{| border="1"
−
''Teaching team: also see the  [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt4wbXL8oVXkSnh_igVcq_gwhErfWo/edit#gid=0 Google Doc Spreadsheet]''
 
−
 
 
−
{|
 
 
|+ Schedule for 10-601  
 
|+ Schedule for 10-601  
 
!  Date  
 
!  Date  
Line 13: Line 10:
 
!  Lecturer  
 
!  Lecturer  
 
!  Assignment
 
!  Assignment
−
|M 1/11 || [[10-601 Course Overview]] | Nina |  
+
!  TAs
 +
|-
 +
| M 1/11 || [[10-601 Course Overview|Course Overview]] || Nina || ||
 +
|-
 +
| W 1/13 || [[10-601 Introduction to Probability|Intro to Probability]] || William
 +
|| [http://curtis.ml.cmu.edu/w/courses/images/8/88/Homework1.pdf HW1 Background Test]
 +
|| Will, Han
 +
|-
 +
| M 1/18 ||colspan="4"| ''Martin Luther King Day''
 +
|-
 +
| W 1/20 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William
 +
|| [http://curtis.ml.cmu.edu/w/courses/images/c/c0/10601b-s16-homework2.pdf HW2: implementing naive Bayes] || Travis, Maria
 +
|-
 +
| M 1/25 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  ||
 +
|-
 +
| W 1/27 ||  [[10-601 Linear Regression|Linear Regression]] || William  || ||
 +
|-
 +
| M 2/1 || [[10-601B Perceptrons and Large Margin|Perceptrons and Large Margin]]  || Nina ||  ||  
 
|-
 
|-
−
| W 1/13 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William|| HW1 - [ http://www.cs.cmu.edu/~ninamf/courses/601sp15/hw/homework1.pdfwill be similar to this] TAs: Will, Han
+
| W 2/3 || [[10-601B Kernels|Kernels]] || Nina || [http://curtis.ml.cmu.edu/w/courses/images/6/6e/10601-homework-3.pdf HW3: logistic and linear regression]|| Tianshu, Will 
 +
|-
 +
| M 2/8 || [[10-601B Kernelized SVMs | Kernelized SVMs]] and [[10-601B Intro to neural Networks | Intro to Neural Networks]] || Nina || ||
 
|-  
 
|-  
−
| M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William ||
+
| W 2/10 || [[10-601B Neural Networks|Neural Networks]] || Nina || ||
 +
|-
 +
| M 2/15 || [[10-601B AdaBoost | AdaBoost]] || Nina || ||
 
|-  
 
|-  
−
| 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] (due Sept. 18th via Autolab)
+
| W 2/17 || [[10-601B Generalization and Overfitting: Sample Complexity Results for Supervised Classification | Generalization and Overfitting: Sample Complexity Results for Supervised Classification]] || Nina ||  [http://curtis.ml.cmu.edu/w/courses/images/2/25/10601-Homework-4.pdf HW4: SVM, ANN, Boosting] [http://curtis.ml.cmu.edu/w/courses/images/4/44/Hw4_adaboost.zip HW4 code] || Han, Tianshu
 
|-  
 
|-  
−
| M 9/16 || [[10-601 Logistic Regression|Logistic Regression]] || William ||  
+
| M 2/22 || [[10-601B Generalization and Overfitting: Sample Complexity Results for Supervised Classification 2 | Generalization and Overfitting: Sample Complexity Results for Supervised Classification 2]] || Nina || ||
 
|-  
 
|-  
−
| W 9/18 || [[10-601 SVMS|SVMs and Margin Classifiers]] || William ||   HW3: [http://curtis.ml.cmu.edu/w/courses/images/c/ce/10601-13F-assignment_3.pdf Logistic Regression]  Download: [http://curtis.ml.cmu.edu/w/courses/images/3/3a/Handout.mat data]  (due Sept. 25th via Autolab) [http://curtis.ml.cmu.edu/w/courses/images/d/d4/Hw3_solution_example.zip example solution]
+
| W 2/24|| [[10-601B Model Selection | Model Selection]] and Midterm Review || Nina || ||
 
|-  
 
|-  
−
| M 9/23 || [[10-601 Linear Regression|Linear Regression]] || Eric
+
| M 2/29 ||colspan="4"| ''Midterm exam''
 
|-  
 
|-  
−
| W 9/25 || [http://curtis.ml.cmu.edu/w/courses/images/8/8a/Lecture7-dnn.pdf Neural networks and Deep Belief Networks] || Eric || HW4: [http://curtis.ml.cmu.edu/w/courses/images/a/a2/10601-13F-assignment_4.pdf Linear Regression]  Download: [http://curtis.ml.cmu.edu/w/courses/images/d/da/Assignment4-handout.mat data] (due Oct. 2nd ('''Before lecture''') via Autolab)
+
| W 3/2 || [[10-601B Clustering| Clustering]] || Nina || ||
 +
|-
 +
| M 3/7 ||colspan="4"| ''Spring break''
 +
|-
 +
| W 3/9 ||colspan="4"| ''Spring break''
 +
|-                                                                             
 +
| M 3/14 || [[10-601B Active Learning|Active Learning]] || Nina ||  ||
 
|-  
 
|-  
−
| M 9/30 || [[10-601 K-NN And Trees|K-NN, Decision Trees, and Rule Learning]] || William ||
+
| W 3/16 || [[10-601B SSL| Semi-Supervised Learning]] || William || [http://curtis.ml.cmu.edu/w/courses/images/c/cf/Homework5.pdf HW5: Active learning and clustering] || Travis, Han
 +
|-
 +
| M 3/21 || [[10-601 GM1| Graphical Models 1]] || William || ||     
 
|-                                                                                      
 
|-                                                                                      
−
| 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]
+
| W 3/23 || [[10-601 GM2| Graphical Models 2]] ||  William  || ||  
−
|-                                                                                    
+
|-
−
| M 10/7 || [[10-601 PAC| PAC Learning]] || Eric ''(William out)''
+
| M 3/28 || [[10-601 GM3|Graphical Models 3]] || William || ||  
−
|-                                                                                    
+
|-
−
| 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 3/30 || [[10-601 Sequences|Graphical Models for Sequential Data]] || William ||[http://curtis.ml.cmu.edu/w/courses/images/d/d8/Homework6.pdf HW6: Graphical models] || Maria, Renato
−
|-                                                                                    
+
|-
−
| M 10/14 || [[10-601 Ensembles 1|Ensemble Methods 1]] || William
+
| M 4/4 || [[10-601 Topic Models|Topic Models]] || 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
+
| W 4/6 || [[10-601 Deep Learning 1|Deep Learning 1]] || William || ||  
−
|-                                                                                    
+
|-
−
| M 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Eric    
+
| M 4/11 || [[10-601 Deep Learning 2|Deep Learning 2]] || William || ||
−
|-                                                                                    
 
−
| 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]
 
−
|-                                                                                    
 
−
| 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]
 
−
|-                                                                                    
 
−
| M 11/4 || [[10-601 GM1| Graphical Models 1]] || Eric      
 
−
|-                                                                                    
 
−
| 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)
 
−
|-                                                                                    
 
−
| M 11/11 || [[10-601 Sequences|HMMS, Sequences, and Structured Output Prediction]] || William                                            
 
 
|-
 
|-
−
| W 11/13 || [[10-601 Topic Models|d-separation, Explaining away, and Topic Models]]|| William || Project milestone 4             
+
| W 4/13 || [[10-601_PCA|PCA and dimension reduction]] || William || HW7: Deep Learning || Zichao
 
|-
 
|-
−
| M 11/18 || [[10-601 Network Models| Network Models]] || Eric
+
| M 4/18 || [[10-601_Matrix_Factorization|Matrix Factorization and collaborative filtering]] || William || ||
 
|-
 
|-
−
| W 11/20 || Review Session/Special Topics || Eric
+
| W 4/20 || [[10-601 Reinforcement Learning|Reinforcement Learning]] || Nina || ||
−
|-
 
−
| M 11/25 || [[10-601 Exam|Not-quite-final Exam]] ||  
 
−
[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]
 
 
|-
 
|-
−
| W 11/27 || ''No class - Thanksgiving'' ||
+
| M 4/25 || [[10-601 Review|Review]] || Nina and William || ||
−
|-
 
−
| M 12/2 || [[10-601 Markov Decision Processes and Reinforcement Learning| Markov Decision Processes and Reinforcement Learning]] || Eric
 
 
|-
 
|-
−
| 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]||
+
| W 4/27 || ''In-class final exam'' ||  || ||
−
|-
 
−
| 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.
 
'''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 ==
+
Note from William to William and Nina: there's [[Also_-_a_draft_schedule_for_10-601B| a copy of the old draft, with William's slides and notes, here]]
−
 
 
−
=== Linear Classifiers ===
 
−
 
 
−
A probabilistic view of linear classification:
 
−
 
 
−
* [[10-601 Introduction to Probability]]
 
−
* [[10-601 Naive Bayes]]
 
−
* [[10-601 Logistic Regression]]
 
−
 
 
−
Another view of classification:
 
  
−
* [[10-601 Introduction to Linear Algebra]]
+
== Other Lectures ==
−
* [[10-601 Perceptrons and Voted Perceptrons]]
 
−
* [[10-601 Voted Perceptrons and Support Vector Machines]]
 
  
−
Summary:
 
 
* [[10-601 Wrap-up on Linear Classification]]
 
* [[10-601 Wrap-up on Linear Classification]]

Latest revision as of 21:14, 5 September 2016

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

Schedule

Teaching team only: also see the Google Doc Spreadsheet. Students should not try and decipher the scribbles and planning notes on this gdoc - use the schedule below.

Schedule for 10-601
Date Main Topic of Lecture Lecturer Assignment TAs
M 1/11 Course Overview Nina
W 1/13 Intro to Probability William HW1 Background Test 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 Linear Regression William
M 2/1 Perceptrons and Large Margin Nina
W 2/3 Kernels Nina HW3: logistic and linear regression Tianshu, Will
M 2/8 Kernelized SVMs and Intro to Neural Networks Nina
W 2/10 Neural Networks Nina
M 2/15 AdaBoost Nina
W 2/17 Generalization and Overfitting: Sample Complexity Results for Supervised Classification Nina HW4: SVM, ANN, Boosting HW4 code Han, Tianshu
M 2/22 Generalization and Overfitting: Sample Complexity Results for Supervised Classification 2 Nina
W 2/24 Model Selection and Midterm Review Nina
M 2/29 Midterm exam
W 3/2 Clustering Nina
M 3/7 Spring break
W 3/9 Spring break
M 3/14 Active Learning Nina
W 3/16 Semi-Supervised Learning William HW5: Active learning and clustering Travis, Han
M 3/21 Graphical Models 1 William
W 3/23 Graphical Models 2 William
M 3/28 Graphical Models 3 William
W 3/30 Graphical Models for Sequential Data William HW6: Graphical models Maria, Renato
M 4/4 Topic Models William
W 4/6 Deep Learning 1 William
M 4/11 Deep Learning 2 William
W 4/13 PCA and dimension reduction William HW7: Deep Learning Zichao
M 4/18 Matrix Factorization and collaborative filtering William
W 4/20 Reinforcement Learning Nina
M 4/25 Review Nina and William
W 4/27 In-class final exam

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.

Note from William to William and Nina: there's a copy of the old draft, with William's slides and notes, here

Other Lectures

Retrieved from "http://curtis.ml.cmu.edu/w/courses/index.php?title=Syllabus_for_Machine_Learning_10-601B_in_Spring_2016&oldid=18635"