Syllabus for Machine Learning 10-601 in Fall 2013
From Cohen Courses
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 | Assignment | |
|---|---|---|---|---|
| M 9/2 | No class - Labor day | |||
| W 9/4 | Overview and Intro to Probability | William | HW: Working with Probabilities | |
| M 9/9 | The Naive Bayes algorithm | William | previous HW | |
| W 9/11 | The Perceptron algorithm | William | HW: Implement Naive Bayes and the Perceptron algorithm | |
| Logistic Regression | William | |||
| W 9/18 | Evaluating and comparing classifiers | William | ||
| M 9/23 | Neural networks | Eric | ||
| W 9/25 | K-nearest neighbor classifiers | Eric | ||
| M 9/30 | Decision trees | Eric | ||
| W 10/2 | PAC-learning and learning theory | William | ||
| M 10/7 | Bias-variance and linear regression | William | ||
| W 10/9 | K-means and Mixture models | Eric | ||
| M 10/14 | Dimensionality Reduction | Eric | ||
| W 10/16 | Modeling distributions with Bayes Nets and Markov Fields | Eric | ||
| M 10/21 | Learning with graphical models | Eric | ||
| W 10/23 | Topic models | William | ||
| M 10/28 | HMMs | Eric | ||
| W 10/30 | ||||
| M 11/4 | ||||
| W 11/6 | ||||
| M 11/11 | ||||
| W 11/13 | ||||
| M 11/18 | ||||
| W 11/20 | ||||
| M 11/25 | ||||
| W 11/27 | Thanksgiving - class cancelled | |||
| M 12/2 | ||||
| W 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:
