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

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| W 1/27 || [[10-601 Perceptrons and Voted Perceptrons|Perceptrons]] and [[10-601 SVMS|SVMs]]  || William ||  ||  
 
| W 1/27 || [[10-601 Perceptrons and Voted Perceptrons|Perceptrons]] and [[10-601 SVMS|SVMs]]  || William ||  ||  
 
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| M 9/23 ||  [[10-601 Linear Regression|Linear Regression]] || Eric
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| M 2/1 || [[10-601 Kernels]]  || Nina ||  ||
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| W 2/3 ||  [[10-601 Linear Regression|Linear Regression]] || Nina
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|| HW3: implementing logistic regression and kernel perceptrons || Tianshu, Will
 
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| 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 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)

Revision as of 15:37, 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
W 9/25 Neural networks and Deep Belief Networks Eric HW4: Linear Regression Download: data (due Oct. 2nd (Before lecture) via Autolab)
M 9/30 K-NN, Decision Trees, and Rule Learning William
W 10/2 Evaluating and Comparing Classifiers Experimentally William HW5: Compare classifiers Download: data1 data2 (due Oct. 9th (Before lecture) via Autolab) Example code: [1]
M 10/7 PAC Learning Eric (William out)
W 10/9 Bias-Variance Decomposition Eric (William out) HW6: PAC and VC dimension
M 10/14 Ensemble Methods 1 William
W 10/16 Ensemble Methods 2 William Project description: [2] 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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