Difference between revisions of "Class meeting for 10-605 in Fall 2016 Probability Review"
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| − | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in | + | This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2016]]. |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/ | + | |
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/2016/prob-tour+bayes.pptx Slides in Powerpoint] | ||
| + | * [http://www.cs.cmu.edu/~wcohen/10-605/2016/prob-tour+bayes.pdf Slides in PDF] | ||
=== Readings for the Class === | === Readings for the Class === | ||
| − | * | + | * Optional: Mitchell 6.1-6.10 |
| + | |||
| + | === Today's quiz === | ||
| + | |||
| + | * [https://qna-app.appspot.com/edit_new.html#/pages/view/aglzfnFuYS1hcHByGQsSDFF1ZXN0aW9uTGlzdBiAgIDQqZ35Cgw] | ||
| + | |||
| + | === 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 | ||
Latest revision as of 16:02, 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 2016.
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
