Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
From Cohen Courses
Jump to navigationJump to search (Created page with "This is the syllabus for Machine Learning 10-601 in Spring 2016. === Schedule === ''Teaching team: also see the [https://docs.google.com/spreadsheets/d/1CNT4I-nSFBxqNRt...") |
|||
Line 11: | Line 11: | ||
! Lecturer | ! Lecturer | ||
! Assignment | ! Assignment | ||
− | |||
− | |||
|- | |- | ||
| W 9/4 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William|| HW1: [http://curtis.ml.cmu.edu/w/courses/images/0/04/10601-S13_Assignment_1.pdf worksheet on probabilities] (due Sept. 13th via BlackBoard) | | W 9/4 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William|| HW1: [http://curtis.ml.cmu.edu/w/courses/images/0/04/10601-S13_Assignment_1.pdf worksheet on probabilities] (due Sept. 13th via BlackBoard) |
Revision as of 15:07, 6 January 2016
This is the syllabus for Machine Learning 10-601 in Spring 2016.
Schedule
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: