Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
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=== Schedule === | === Schedule === | ||
− | '' | + | ''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. |
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{| border="1" | {| border="1" | ||
|+ Schedule for 10-601 | |+ Schedule for 10-601 | ||
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! Lecturer | ! Lecturer | ||
! Assignment | ! Assignment | ||
+ | ! 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 1 | + | | M 2/1 || [[10-601B Perceptrons and Large Margin|Perceptrons and Large Margin]] || Nina || || |
|- | |- | ||
− | | W | + | | 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 | + | | W 2/10 || [[10-601B Neural Networks|Neural Networks]] || Nina || || |
+ | |- | ||
+ | | M 2/15 || [[10-601B AdaBoost | AdaBoost]] || Nina || || | ||
|- | |- | ||
− | | W | + | | 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 | + | | 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 | + | | W 2/24|| [[10-601B Model Selection | Model Selection]] and Midterm Review || Nina || || |
|- | |- | ||
− | | M | + | | M 2/29 ||colspan="4"| ''Midterm exam'' |
|- | |- | ||
− | | W | + | | 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 | + | | 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 | + | | W 3/23 || [[10-601 GM2| Graphical Models 2]] || William || || |
− | + | |- | |
− | + | | 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 | |
− | |- | + | |- |
− | | M | + | | M 4/4 || [[10-601 Topic Models|Topic Models]] || William || || |
− | + | |- | |
− | + | | W 4/6 || [[10-601 Deep Learning 1|Deep Learning 1]] || William || || | |
− | + | |- | |
− | + | | M 4/11 || [[10-601 Deep Learning 2|Deep Learning 2]] || William || || | |
− | |- | ||
− | | W | ||
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− | |- | ||
− | | M | ||
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− | |- | ||
− | | M | ||
|- | |- | ||
− | | W | + | | W 4/13 || [[10-601_PCA|PCA and dimension reduction]] || William || HW7: Deep Learning || Zichao |
|- | |- | ||
− | | M | + | | M 4/18 || [[10-601_Matrix_Factorization|Matrix Factorization and collaborative filtering]] || William || || |
|- | |- | ||
− | | W | + | | W 4/20 || [[10-601 Reinforcement Learning|Reinforcement Learning]] || Nina || || |
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|- | |- | ||
− | + | | M 4/25 || [[10-601 Review|Review]] || Nina and William || || | |
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− | | M | ||
|- | |- | ||
− | | W | + | | W 4/27 || ''In-class final exam'' || || || |
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|} | |} | ||
'''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. | ||
− | + | 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]] | |
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− | + | == Other Lectures == | |
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* [[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.
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