Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
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| W 1/13 || [[10-601 Introduction to Probability|Intro to Probability]] || William | | W 1/13 || [[10-601 Introduction to Probability|Intro to Probability]] || William | ||
− | || [http://curtis.ml.cmu.edu/w/courses/images/ | + | || [http://curtis.ml.cmu.edu/w/courses/images/8/88/Homework1.pdf HW1 Background Test] |
|| Will, Han | || Will, Han | ||
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| W 1/20 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William | | W 1/20 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William | ||
− | || HW2: implementing naive Bayes || Travis, Maria | + | || [http://curtis.ml.cmu.edu/w/courses/images/c/c0/10601b-s16-homework2.pdf HW2: implementing naive Bayes] || Travis, Maria |
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| M 1/25 || [[10-601 Logistic Regression|Logistic Regression]] || William || || | | M 1/25 || [[10-601 Logistic Regression|Logistic Regression]] || William || || | ||
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| W 1/27 || [[10-601 Linear Regression|Linear Regression]] || William || || | | W 1/27 || [[10-601 Linear Regression|Linear Regression]] || William || || | ||
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− | | M 2/1 || [[10-601B Perceptrons and | + | | M 2/1 || [[10-601B Perceptrons and Large Margin|Perceptrons and Large Margin]] || Nina || || |
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− | | W 2/3 || [[10-601B Kernels|Kernels]] || Nina || HW3: | + | | 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 |
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− | | M 2/8 || [[10-601B | + | | M 2/8 || [[10-601B Kernelized SVMs | Kernelized SVMs]] and [[10-601B Intro to neural Networks | Intro to Neural Networks]] || Nina || || |
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− | | W 2/10 || [[10-601B | + | | W 2/10 || [[10-601B Neural Networks|Neural Networks]] || Nina || || |
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− | | M 2/15 || [[10-601B | + | | M 2/15 || [[10-601B AdaBoost | AdaBoost]] || Nina || || |
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− | | W 2/17 || [[10-601B | + | | 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 |
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− | | M 2/22 || [[10-601B | + | | 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 || || |
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− | | W 2/24|| Midterm Review || Nina || || | + | | W 2/24|| [[10-601B Model Selection | Model Selection]] and Midterm Review || Nina || || |
|- | |- | ||
| M 2/29 ||colspan="4"| ''Midterm exam'' | | M 2/29 ||colspan="4"| ''Midterm exam'' | ||
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− | | W 3/2 || [[10-601B Clustering| | + | | W 3/2 || [[10-601B Clustering| Clustering]] || Nina || || |
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− | |||
− | |||
− | |||
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− | | M 3/ | + | | M 3/7 ||colspan="4"| ''Spring break'' |
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− | | W 3/ | + | | W 3/9 ||colspan="4"| ''Spring break'' |
+ | |- | ||
+ | | M 3/14 || [[10-601B Active Learning|Active Learning]] || Nina || || | ||
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+ | | 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 || || | | M 3/21 || [[10-601 GM1| Graphical Models 1]] || William || || | ||
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| W 3/23 || [[10-601 GM2| Graphical Models 2]] || William || || | | W 3/23 || [[10-601 GM2| Graphical Models 2]] || William || || | ||
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− | | M 3/28 || [[10-601 Sequences|Graphical Models for Sequential Data]] || William ||HW6: Graphical models || Maria, Renato | + | | M 3/28 || [[10-601 GM3|Graphical Models 3]] || William || || |
+ | |- | ||
+ | | 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 | ||
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| M 4/4 || [[10-601 Topic Models|Topic Models]] || William || || | | M 4/4 || [[10-601 Topic Models|Topic Models]] || William || || | ||
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− | | W 4/6 || Deep Learning 1 || William || || | + | | W 4/6 || [[10-601 Deep Learning 1|Deep Learning 1]] || William || || |
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− | | M 4/11 || Deep Learning 2 || William || || | + | | M 4/11 || [[10-601 Deep Learning 2|Deep Learning 2]] || William || || |
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− | | W 4/13 || [[10- | + | | W 4/13 || [[10-601_PCA|PCA and dimension reduction]] || William || HW7: Deep Learning || Zichao |
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| M 4/18 || [[10-601_Matrix_Factorization|Matrix Factorization and collaborative filtering]] || William || || | | M 4/18 || [[10-601_Matrix_Factorization|Matrix Factorization and collaborative filtering]] || William || || | ||
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| W 4/20 || [[10-601 Reinforcement Learning|Reinforcement Learning]] || Nina || || | | W 4/20 || [[10-601 Reinforcement Learning|Reinforcement Learning]] || Nina || || | ||
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− | | M 4/25 || Review || Nina and William || || | + | | M 4/25 || [[10-601 Review|Review]] || Nina and William || || |
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− | | W 4/27 || '' | + | | W 4/27 || ''In-class final exam'' || || || |
|} | |} | ||
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.
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