Difference between revisions of "10-601B Kernelized SVMs"
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* Support Vector Machines: Bishop 7.1, Murphy 14.5 | * Support Vector Machines: Bishop 7.1, Murphy 14.5 | ||
| − | * [http://cs229.stanford.edu/notes/cs229-notes3.pdf | + | * [http://cs229.stanford.edu/notes/cs229-notes3.pdf Andrew Ng's notes on SVM optimization] |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Revision as of 22:33, 8 February 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Support Vector Machines: Bishop 7.1, Murphy 14.5
- Andrew Ng's notes on SVM optimization
What You Should Know Afterward
- What functions can be expressed with multi-layer networks that a single layer cannot express
- The backpropagation algorithm, and what loss is associated with it
- In outline, how deep neural networks are trained
