Difference between revisions of "Class meeting for 10-605 SSL on Graphs"
From Cohen Courses
Jump to navigationJump to search| (9 intermediate revisions by the same user not shown) | |||
| 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 === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/ssl- | + | * [http://www.cs.cmu.edu/~wcohen/10-605/ssl-on-graphs.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/ssl-on-graphs.pdf PDF]. |
| − | * [ | + | |
| + | === Quiz === | ||
| + | |||
| + | * [https://qna.cs.cmu.edu/#/pages/view/92 Today's quiz] | ||
=== Optional Readings === | === Optional Readings === | ||
| Line 11: | Line 14: | ||
* [https://server1.tepper.cmu.edu/seminars/docs/BinderPartha.pdf PP Talukdar, K Crammer (2009):] New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457 | * [https://server1.tepper.cmu.edu/seminars/docs/BinderPartha.pdf PP Talukdar, K Crammer (2009):] New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457 | ||
* [http://www.cs.cmu.edu/~wcohen/postscript/ai-stats-2014.pdf Partha Pratim Talukdar and William W. Cohen (2014)]: Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch in AI-Stats 2014. | * [http://www.cs.cmu.edu/~wcohen/postscript/ai-stats-2014.pdf Partha Pratim Talukdar and William W. Cohen (2014)]: Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch in AI-Stats 2014. | ||
| + | * Sujith Ravi and Qiming Diao. "Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation." arXiv preprint arXiv:1512.01752 (2015). | ||
=== Key things to remember === | === Key things to remember === | ||
| − | * The harmonic fields and multi-rank walk SSL algorithms. | + | * What SSL is and when it is useful. |
| + | * The harmonic fields and multi-rank walk SSL algorithms, and properties of these algorithms. | ||
| + | * What is optimized by the MAD algorithm, and what the goal is of the various terms in the optimization. | ||
| + | * The power iteration clustering algorithm. | ||
Latest revision as of 12:20, 14 November 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
Quiz
Optional Readings
- Frank Lin and William W. Cohen (2010): Semi-Supervised Classification of Network Data Using Very Few Labels in ASONAM-2010.
- PP Talukdar, K Crammer (2009): New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457
- Partha Pratim Talukdar and William W. Cohen (2014): Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch in AI-Stats 2014.
- Sujith Ravi and Qiming Diao. "Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation." arXiv preprint arXiv:1512.01752 (2015).
Key things to remember
- What SSL is and when it is useful.
- The harmonic fields and multi-rank walk SSL algorithms, and properties of these algorithms.
- What is optimized by the MAD algorithm, and what the goal is of the various terms in the optimization.
- The power iteration clustering algorithm.
