Difference between revisions of "10-601 Clustering"
From Cohen Courses
Jump to navigationJump to search| (14 intermediate revisions by 3 users not shown) | |||
| Line 1: | Line 1: | ||
| − | This a | + | This a pair of lectures used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]]. |
=== Slides === | === Slides === | ||
| − | [http:// | + | * Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/clusteringH.pdf Slides in pdf]. |
| + | |||
| + | * Bhavana's lecture on 13th October 2014 [[Media:Kmeans_13october2014_dalvi.pptx]] Slides in PPT | ||
| + | * Bhavana's lecture on 16th October 2014 [[Media:Kmeans_16october2014_dalvi.pptx]] Slides in PPT | ||
| + | * Combined PDF version for classes on 13th and 16th October [[Media:Kmeans_cs601_CMU_dalvi.pdf]] Slides in PDF (Only the slides on advanced topics vary across lectures) | ||
=== Readings === | === Readings === | ||
| − | + | Mitchell 6.12 - a nice description of EM and k-means. | |
| − | |||
| − | Mitchell 6.12 | ||
| + | === What You Should Know Afterward === | ||
You should know how to implement these methods, and what their relative advantages and disadvantages are. | You should know how to implement these methods, and what their relative advantages and disadvantages are. | ||
* Overview of clustering | * Overview of clustering | ||
Latest revision as of 15:49, 6 January 2016
This a pair of lectures used in the Syllabus for Machine Learning 10-601B in Spring 2016.
Slides
- Ziv's lecture: Slides in pdf.
- Bhavana's lecture on 13th October 2014 Media:Kmeans_13october2014_dalvi.pptx Slides in PPT
- Bhavana's lecture on 16th October 2014 Media:Kmeans_16october2014_dalvi.pptx Slides in PPT
- Combined PDF version for classes on 13th and 16th October Media:Kmeans_cs601_CMU_dalvi.pdf Slides in PDF (Only the slides on advanced topics vary across lectures)
Readings
Mitchell 6.12 - a nice description of EM and k-means.
What You Should Know Afterward
You should know how to implement these methods, and what their relative advantages and disadvantages are.
- Overview of clustering
- Distance functions and similarity measures and their impact
- K-means algorithms
- How to chose k and what is the impact of large and small k's
- EM
- Differences between GM and K-means
