Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
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=== Schedule === | === Schedule === | ||
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+ | '''Note: the days for each lecture will be adjusted - these are the dates from 2013.''' | ||
''Teaching team: also see the [https://docs.google.com/spreadsheet/ccc?key=0AqbWt5nnjNrYdEFheHNkVHRrWnRncV9fN2VST0VvR1E&usp=sharing| Google Doc Spreadsheet]'' | ''Teaching team: also see the [https://docs.google.com/spreadsheet/ccc?key=0AqbWt5nnjNrYdEFheHNkVHRrWnRncV9fN2VST0VvR1E&usp=sharing| Google Doc Spreadsheet]'' | ||
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| M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William || | | M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William || | ||
|- | |- | ||
− | | 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] | + | | 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) |
|- | |- | ||
| M 9/16 || [[10-601 Logistic Regression|Logistic Regression]] || William || | | M 9/16 || [[10-601 Logistic Regression|Logistic Regression]] || William || | ||
|- | |- | ||
− | | 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) | + | | 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] |
|- | |- | ||
− | | M 9/23 || [[10-601 Linear Regression|Linear Regression] | + | | M 9/23 || [[10-601 Linear Regression|Linear Regression]] || Eric |
|- | |- | ||
| 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) | ||
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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 || | ||
|- | |- | ||
− | | W 10/2 || [[10-601 Evaluation|Evaluating and Comparing Classifiers Experimentally]] || William || HW5: | + | | 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] |
|- | |- | ||
− | | M 10/7 || | + | | M 10/7 || [[10-601 PAC| PAC Learning]] || Eric ''(William out)'' |
|- | |- | ||
− | | W 10/9 || Bias-Variance Decomposition || Eric ''(William out)'' || HW6: | + | | 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 ] |
|- | |- | ||
− | | M 10/14 || Ensemble | + | | M 10/14 || [[10-601 Ensembles 1|Ensemble Methods 1]] || William |
|- | |- | ||
− | | W 10/16 || Ensemble | + | | 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 || Unsupervised Learning: k-Means and Mixtures || Eric | + | | M 10/21 || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || Eric |
|- | |- | ||
− | | W 10/23 || Unsupervised Learning: Dimensionality Reduction|| Eric || Project milestone | + | | 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 || Semi-Supervised Learning || William | + | | M 10/28 || [[10-601 SSL| Semi-Supervised Learning]] || William |
|- | |- | ||
− | | W 10/30 || Collaborative Filtering and Matrix Factorization || William || Project milestone | + | | 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 || Graphical Models 1 || Eric | + | | M 11/4 || [[10-601 GM1| Graphical Models 1]] || Eric |
|- | |- | ||
− | | W 11/6 || Graphical Models 2 || 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 || HMMS, Sequences, and Structured Output Prediction || William | + | | M 11/11 || [[10-601 Sequences|HMMS, Sequences, and Structured Output Prediction]] || William |
|- | |- | ||
− | | W 11/13 || Topic Models|| | + | | W 11/13 || [[10-601 Topic Models|d-separation, Explaining away, and Topic Models]]|| William || Project milestone 4 |
|- | |- | ||
− | | M 11/18 || | + | | M 11/18 || [[10-601 Network Models| Network Models]] || Eric |
|- | |- | ||
| W 11/20 || Review Session/Special Topics || Eric | | W 11/20 || Review Session/Special Topics || Eric | ||
|- | |- | ||
− | | M 11/25 || | + | | 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'' || | | W 11/27 || ''No class - Thanksgiving'' || | ||
|- | |- | ||
− | | M 12/2 || Markov Decision Processes and Reinforcement Learning || Eric | + | | M 12/2 || [[10-601 Markov Decision Processes and Reinforcement Learning| Markov Decision Processes and Reinforcement Learning]] || Eric |
|- | |- | ||
− | | W 12/4 || Scalable Learning and Parallelization || William || | + | | 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]|| |
|- | |- | ||
− | | | + | | 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 == | == Section-by-Section == |
Latest revision as of 12:48, 18 September 2014
This is the syllabus for Machine Learning 10-601 in Fall 2013.
Contents
Prezi Overview of All the Topics in the Course
Schedule
Note: the days for each lecture will be adjusted - these are the dates from 2013.
Teaching team: also see the Google Doc Spreadsheet
Date of lecture | Topic | Lecturer | Assignment | |
---|---|---|---|---|
M 9/2 | No class - Labor day | |||
W 9/4 | Overview and Intro to Probability | William | HW1: worksheet on probabilities (due Sept. 13th via BlackBoard) | |
M 9/9 | The Naive Bayes algorithm | William | ||
W 9/11 | The Perceptron algorithm | William | HW2:Naive Bayes & Voted Perceptron (due Sept. 18th via Autolab) | |
M 9/16 | Logistic Regression | William | ||
W 9/18 | SVMs and Margin Classifiers | William | HW3: Logistic Regression Download: data (due Sept. 25th via Autolab) example solution | |
M 9/23 | Linear Regression | Eric | ||
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 | |||
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:
- 10-601 Introduction to Linear Algebra
- 10-601 Perceptrons and Voted Perceptrons
- 10-601 Voted Perceptrons and Support Vector Machines
Summary: