Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
From Cohen Courses
Jump to navigationJump to searchLine 37: | Line 37: | ||
| 10/7 || Bias-variance and linear regression || William | | 10/7 || Bias-variance and linear regression || William | ||
|- | |- | ||
− | | 10/9 || K-means and Mixture models || | + | | 10/9 || K-means and Mixture models || Eric |
|- | |- | ||
| 10/14 || Dimensionality Reduction || Eric | | 10/14 || Dimensionality Reduction || Eric |
Revision as of 15:44, 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 | Evaluating and comparing classifiers | William | ||
9/23 | Neural networks | Eric | ||
9/25 | K-nearest neighbor classifiers | Eric | ||
9/30 | Decision trees | Eric | ||
10/2 | PAC-learning and learning theory | William | ||
10/7 | Bias-variance and linear regression | William | ||
10/9 | K-means and Mixture models | Eric | ||
10/14 | Dimensionality Reduction | Eric | ||
10/16 | Modeling distributions with Bayes Nets and Markov Fields | Eric | ||
10/21 | Learning with graphical models | Eric | ||
10/23 | Topic models | William | ||
10/28 | HMMs | Eric | ||
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: