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|Overview and Intro to Probability]] || William|| | | W 1/13 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William|| | ||
− | HW1 [http://www.cs.cmu.edu/~ninamf/courses/601sp15/hw/homework1. | + | HW1 [http://www.cs.cmu.edu/~ninamf/courses/601sp15/hw/homework1.pdf will be similar to this] TAs: Will, Han |
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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 || |
Revision as of 15:21, 6 January 2016
This is the syllabus for Machine Learning 10-601 in Spring 2016.
Schedule
In progress....
Teaching team: also see the Google Doc Spreadsheet
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: