Difference between revisions of "Class meeting for 10-605 in Fall 2016 Probability Review"
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=== Readings for the Class === | === Readings for the Class === | ||
| − | * | + | * Optional: Mitchell 6.1-6.10 |
=== Things to remember === | === Things to remember === | ||
Revision as of 14:27, 1 August 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall 2016.
Slides
Readings for the Class
- Optional: Mitchell 6.1-6.10
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
