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Support Vector Machine (SVM)
Support Vector Machine (SVM)
Support Vector Machine (SVM) in 2 minutes : https://www.youtube.com/watch?v=_YPScrckx28
Support Vector Machine (SVM) in 7 minutes - Fun Machine Learning : https://www.youtube.com/watch?v=Y6RRHw9uN9o
Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) : https://www.youtube.com/watch?v=lDwow4aOrtg
16. Learning: Support Vector Machines (MIT OpenCourseWare): https://www.youtube.com/watch?v=_PwhiWxHK8o
Class 1-Statistical Learning Theory and Applications
Class 1-Statistical Learning Theory and Applications
The course covers foundations and recent advances in machine learning from the point of view of statistical learning and regularization theory.
We will make extensive use of basic notions of calculus, linear algebra, and probability. The essentials are covered in class and the math camp material. We will introduce a few concepts in functional/convex analysis and optimization. Note that this is an advanced graduate course and some exposure to introductory Machine Learning concepts or courses is expected. Students are also expected to have basic familiarity with MATLAB/Octave.
Class01_2018.pdfClass 1- Slides
Class 1- Slides
Statistical Learning Theory and Applications
class02_SLT.pdfClass 2- Slides
Class 2- Slides
Statistical Learning Setting
Class03_RLS.pdfClass 3- Slides
Class 3- Slides
Regularized Least Squares
Class04_Feature+Kernels.pdfClass 4- Slides
Class 4- Slides
Features and Kernels
Class05_LogisticsSVM.pdfClass5(Logistic Regression and Support Vector Machines)
Class06_SGD.pdfClass 06: Learning with Stochastic Gradients
Class 07: Iterative Regularization via Early Stopping
Class08_Sketching.pdfClass8-Learning with (Random) Projections
Class09_Sparsity.pdfClass9-Sparsity Based Regularization
Class10_NN.pdfClass10-Neural Networks
Class11_nn2.pdfClass11-Neural Networks
Class12_GAN.pdfClass12-Generative Adversarial Networks
Class13_SL.pdfClass14_SL.pdfPage updated
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