Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
From Cohen Courses
Jump to navigationJump to searchLine 2: | Line 2: | ||
=== Schedule === | === Schedule === | ||
+ | |||
+ | In ro | ||
''Teaching team: also see the [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt4wbXL8oVXkSnh_igVcq_gwhErfWo/edit#gid=0 Google Doc Spreadsheet]'' | ''Teaching team: also see the [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt4wbXL8oVXkSnh_igVcq_gwhErfWo/edit#gid=0 Google Doc Spreadsheet]'' | ||
Line 11: | Line 13: | ||
! Lecturer | ! Lecturer | ||
! Assignment | ! Assignment | ||
+ | |M 1/11 || [[10-601 Course Overview]] | Nina | | ||
|- | |- | ||
− | | W | + | | 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.pdfwill be similar to this] TAs: Will, Han |
|- | |- | ||
| 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 15:17, 6 January 2016
This is the syllabus for Machine Learning 10-601 in Spring 2016.
Schedule
In ro
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: