Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
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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 | ||
− | || [http://curtis.ml.cmu.edu/w/courses/images/c/c0/10601b-s16-homework2.pdf HW2: implementing naive Bayes] || Travis, Maria | + | || [http://curtis.ml.cmu.edu/w/courses/images/c/c0/10601b-s16-homework2.pdf HW2: implementing naive Bayes] [http://curtis.ml.cmu.edu/w/courses/images/b/be/Hw2_solutions.pdf HW2 Solutions|| Travis, Maria |
|- | |- | ||
| M 1/25 || [[10-601 Logistic Regression|Logistic Regression]] || William || || | | M 1/25 || [[10-601 Logistic Regression|Logistic Regression]] || William || || |
Revision as of 12:42, 24 February 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