Difference between revisions of "Class meeting for 10-605 Probability Review"
From Cohen Courses
Jump to navigationJump to search| Line 1: | Line 1: | ||
| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. |
=== Slides === | === Slides === | ||
Revision as of 16:04, 10 August 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2017.
Slides
Readings for the Class
- Optional: Mitchell 6.1-6.10
Today's quiz
Things to remember
- The joint probability distribution
- Brute-force estimation of a joint distribution
- Density estimation and how it can be used for classification
- Naive Bayes and the conditional independence assumption
- Asymptotic complexity of naive Bayes
- What are streaming machine learning algorithms: ML algorithms that never load in the data
