Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
From Cohen Courses
Jump to navigationJump to searchLine 21: | Line 21: | ||
| 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William | | 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William | ||
|- | |- | ||
− | | 9/11 || [[10-601 | + | | 9/11 || [[10-601 Logistic Regression|Logistic Regression]] || William |
|- | |- | ||
− | | 9/16 || [[10-601 | + | | 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William |
|- | |- | ||
− | | 9/18 | + | | 9/18 |
− | + | |- | |
+ | | 9/23 | ||
+ | |- | ||
+ | | 9/25 | ||
+ | |- | ||
+ | | 9/30 | ||
+ | |- | ||
+ | | 10/2 | ||
+ | |- | ||
+ | | 10/7 | ||
+ | | - | ||
+ | | 10/9 | ||
+ | | - | ||
+ | | 10/14 | ||
+ | |- | ||
+ | | 10/16 | ||
+ | |- | ||
+ | | 10/21 | ||
+ | |- | ||
+ | | 10/23 | ||
+ | |- | ||
+ | | 10/28 | ||
+ | |- | ||
+ | | 10/30 | ||
+ | |- | ||
+ | | 11/4 | ||
+ | |- | ||
+ | | 11/6 | ||
+ | |- | ||
+ | | 11/11 | ||
+ | |- | ||
+ | | 11/13 | ||
+ | |- | ||
+ | | 11/18 | ||
+ | |- | ||
+ | | 11/20 | ||
+ | |- | ||
+ | | 11/25 | ||
+ | |- | ||
+ | | 11/27 || ''Thanksgiving - class cancelled'' || | ||
+ | |- | ||
+ | | 12/2 | ||
+ | |- | ||
+ | | 12/4 | ||
|} | |} | ||
Revision as of 15:36, 31 July 2013
This is the syllabus for Machine Learning 10-601 in Fall 2013.
Contents
Prezi Overview of All the Topics in the Course
Schedule
Date | Topic | Lecturer | Due assignment | New assignment |
---|---|---|---|---|
9/2 | No class - Labor day | |||
9/4 | Overview and Intro to Probability | William | ||
9/9 | The Naive Bayes algorithm | William | ||
9/11 | Logistic Regression | William | ||
9/16 | The Perceptron algorithm | William | ||
9/18 | ||||
9/23 | ||||
9/25 | ||||
9/30 | ||||
10/2 | ||||
10/7 | - | 10/9 | - | 10/14 |
10/16 | ||||
10/21 | ||||
10/23 | ||||
10/28 | ||||
10/30 | ||||
11/4 | ||||
11/6 | ||||
11/11 | ||||
11/13 | ||||
11/18 | ||||
11/20 | ||||
11/25 | ||||
11/27 | Thanksgiving - class cancelled | |||
12/2 | ||||
12/4 |
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: