Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2014"
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! Assignment/Notes | ! Assignment/Notes | ||
|- | |- | ||
− | | Wed 8/27 (Ziv) || Tues 9/2 (Wm) || [[10-601 Introduction to Probability|Overview and | + | | Wed 8/27 (Ziv) || Tues 9/2 (Wm) || [[10-601 Introduction to Probability|Course Overview and Introduction to Probability]] || |
|- | |- | ||
| Wed 9/3 (Ziv) || Thur 9/4 (Wm) || [[10-601 K-NN And Trees|Classification and K-NN]] || ''slides will be updated'' | | Wed 9/3 (Ziv) || Thur 9/4 (Wm) || [[10-601 K-NN And Trees|Classification and K-NN]] || ''slides will be updated'' | ||
Line 40: | Line 40: | ||
| Mon 10/20 (Ziv) || Tues 10/21 (Wm) || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || | | Mon 10/20 (Ziv) || Tues 10/21 (Wm) || [[10-601 Clustering| Unsupervised Learning: k-Means and Mixtures]] || | ||
|- | |- | ||
− | | Wed 10/22 (Ziv) || | + | | Wed 10/22 (Ziv) || Thur 10/23 (Wm) || [[10-601 DR| Unsupervised Learning: Dimensionality Reduction]]|| |
| - | | - | ||
| Mon 10/27 (Ziv) || Tues 10/28 (Wm) || Review session || ''slides to be posted'' | | Mon 10/27 (Ziv) || Tues 10/28 (Wm) || Review session || ''slides to be posted'' | ||
|- | |- | ||
− | | Thurs 10/30 || | + | | Thurs 10/30 || Thur 10/30 || '''Mid-term Exam''' || ''TBA: room and/or time may be different'' |
− | |||
|- | |- | ||
− | | | + | | Mon 11/3 (Ziv) || Tues 11/4 ('''Ziv''') || [[10-601 GM1| Graphical Models 1]] || Ziv's also lecturing in his class on Mon |
− | |||
|- | |- | ||
− | | | + | | Wed 11/5 (Ziv) || Thur 11/6 ('''Ziv''') || [[10-601 GM2| Graphical Models 2]] || Ziv's also lecturing in his class on Wed |
− | |||
|- | |- | ||
− | | | + | | Mon 11/10 (Ziv) || Tues 11/11 ('''Ziv''') || [[10-601 Sequences|HMMS and Sequences]] || Ziv's also lecturing in his class on Mon |
− | |||
|- | |- | ||
− | | | + | | Wed 11/13 ('''Wm''') || Thur 11/13 (Wm) || [[10-601 Topic Models|Matrix Factorization and Topic Models]]|| William's also lecturing in Ziv's class on Wed, ''slides to be updated'' |
− | |||
|- | |- | ||
− | | | + | | Mon 11/17 ('''Wm''') || Tues 11/18 (Wm) || [[10-601 Network Models| Network Models]] || William's also lecturing in Ziv's class on Mon |
− | |||
|- | |- | ||
− | | | + | | Wed 11/19 (Wm) || Thur 11/20 (Wm)|| Semi-supervised learning || William's also lecturing in Ziv's class on Wed |
− | |||
|- | |- | ||
− | | | + | | Mon 11/24 (Wm) || Tues 11/25 (Wm) || [[10-601 Big Data|Scalable Learning and Parallelization]] || William's also lecturing in Ziv's class on Mon |
− | |||
|- | |- | ||
− | | | + | | Wed 11/26 || Thur 11/27 ||''No class - Thanksgiving'' || |
− | |||
| | | | ||
− | | Tues 12/2 | + | | Tues 12/2 ('''Wm''') || Tues 12/2 (Wm) || Learning and NLP || |
− | |||
|- | |- | ||
− | | | + | | Wed 12/3 ('''Ziv''') || Thurs 12/4 (Ziv) || Learning and Biology || |
− | |||
|- | |- | ||
|} | |} |
Revision as of 17:27, 21 July 2014
This is the syllabus for Machine Learning 10-601 in Fall 2014.
Schedule
Lecture for 601-A | Lecture for 601-B | Topic | Assignment/Notes | |||||
---|---|---|---|---|---|---|---|---|
Wed 8/27 (Ziv) | Tues 9/2 (Wm) | Course Overview and Introduction to Probability | ||||||
Wed 9/3 (Ziv) | Thur 9/4 (Wm) | Classification and K-NN | slides will be updated | |||||
Mon 9/8 (Ziv) | Tues 9/9 (Wm) | Decision Trees, and Rule Learning | slides will be updated | |||||
Wed 9/10 (Ziv) | Thur 9/11 (Wm) | The Naive Bayes algorithm | ||||||
Mon 9/15 (Ziv) | Tues 9/16 (Wm) | Linear Regression | slides will be updated | |||||
Wed 9/17 (Ziv) | Thur 9/18 (Wm) | Logistic Regression | ||||||
Mon 9/22 (Wm) | Tues 9/23 (Wm) | The Perceptron algorithm | William's also lecturing in Ziv's class on Mon | |||||
Wed 9/24 (Ziv) | Thur 9/25 (Wm) | Neural networks and Deep Belief Networks | slides will be updated | |||||
Mon 9/29 (Ziv) | Tues 9/30 (Ziv) | SVMs and Margin Classifiers 1 | Ziv's also lecturing in his class on Mon | |||||
Wed 10/1 (Ziv) | Thur 10/2 (Ziv) | SVMs and Margin Classifiers 2 | Ziv's also lecturing in his class on Wed | |||||
Mon 10/6 (Ziv) | Tues 10/7 (Wm) | Evaluating and Comparing Classifiers Experimentally | ||||||
Wed 10/8 (Ziv) | Thus 10/9 (Wm) | PAC Learning | ||||||
Mon 10/13 (Wm) | Tues 10/14 (Wm) | Bias-Variance Decomposition | William's also lecturing in Ziv's class on Mon | |||||
Wed 10/16 (Ziv) | Thur 10/16 (Wm) | Ensemble Methods 1, Ensemble Methods 2 | slides to be updated | |||||
Mon 10/20 (Ziv) | Tues 10/21 (Wm) | Unsupervised Learning: k-Means and Mixtures | ||||||
Wed 10/22 (Ziv) | Thur 10/23 (Wm) | Unsupervised Learning: Dimensionality Reduction | - | Mon 10/27 (Ziv) | Tues 10/28 (Wm) | Review session | slides to be posted | |
Thurs 10/30 | Thur 10/30 | Mid-term Exam | TBA: room and/or time may be different | |||||
Mon 11/3 (Ziv) | Tues 11/4 (Ziv) | Graphical Models 1 | Ziv's also lecturing in his class on Mon | |||||
Wed 11/5 (Ziv) | Thur 11/6 (Ziv) | Graphical Models 2 | Ziv's also lecturing in his class on Wed | |||||
Mon 11/10 (Ziv) | Tues 11/11 (Ziv) | HMMS and Sequences | Ziv's also lecturing in his class on Mon | |||||
Wed 11/13 (Wm) | Thur 11/13 (Wm) | Matrix Factorization and Topic Models | William's also lecturing in Ziv's class on Wed, slides to be updated | |||||
Mon 11/17 (Wm) | Tues 11/18 (Wm) | Network Models | William's also lecturing in Ziv's class on Mon | |||||
Wed 11/19 (Wm) | Thur 11/20 (Wm) | Semi-supervised learning | William's also lecturing in Ziv's class on Wed | |||||
Mon 11/24 (Wm) | Tues 11/25 (Wm) | Scalable Learning and Parallelization | William's also lecturing in Ziv's class on Mon | |||||
Wed 11/26 | Thur 11/27 | No class - Thanksgiving | Tues 12/2 (Wm) | Tues 12/2 (Wm) | Learning and NLP | |||
Wed 12/3 (Ziv) | Thurs 12/4 (Ziv) | Learning and Biology |
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