Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
From Cohen Courses
Jump to navigationJump to searchLine 7: | Line 7: | ||
=== Schedule === | === Schedule === | ||
− | ''TAs and Eric: For now, let's use the [https://docs.google.com/spreadsheet/ccc?key=0AqbWt5nnjNrYdEFheHNkVHRrWnRncV9fN2VST0VvR1E&usp=sharing| Google Doc Spreadsheet] to plan the lectures. Later we can migrate to the wiki schedule below | + | ''TAs and Eric: For now, let's use the [https://docs.google.com/spreadsheet/ccc?key=0AqbWt5nnjNrYdEFheHNkVHRrWnRncV9fN2VST0VvR1E&usp=sharing| Google Doc Spreadsheet] to plan the lectures. Later we can migrate to the wiki schedule below - but it's a little hard to swap things around in the wiki format'' |
{| | {| |
Revision as of 11:13, 1 August 2013
This is the syllabus for Machine Learning 10-601 in Fall 2013.
Contents
Prezi Overview of All the Topics in the Course
Schedule
TAs and Eric: For now, let's use the Google Doc Spreadsheet to plan the lectures. Later we can migrate to the wiki schedule below - but it's a little hard to swap things around in the wiki format
Date | Topic | Lecturer | Assignment |
---|---|---|---|
M 9/2 | No class - Labor day | ||
W 9/4 | Overview and Intro to Probability | William | HW: worksheet on probabilities |
M 9/9 | The Naive Bayes algorithm | William | |
W 9/11 | The Perceptron algorithm | William | HW: Implement two learners |
M 9/16 | The Perceptrons, SVMs, and other Margin Classifiers | William | |
W 9/18 | Logistic Regression | William | HW: Implement two learners |
M 9/23 | |||
W 9/25 | |||
M 9/30 | |||
W 10/2 | |||
M 10/7 | .... | Eric (William out) | |
W 10/9 | ... | Eric (William out) | |
M 10/14 | |||
W 10/16 | |||
M 10/21 | |||
W 10/23 | |||
M 10/28 | |||
W 10/30 | |||
M 11/4 | |||
W 11/6 | |||
M 11/11 | |||
W 11/13 | ... | Eric (William out) | |
M 11/18 | |||
W 11/20 | |||
M 11/25 | |||
W 11/27 | No class - Thanksgiving | ||
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: