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Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data.
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Description
Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.
Course content
- The Course at a Glance Summary Unlimited
- The Learning Problem in Perspective Unlimited
- Regularization and Reproducing Kernel Hilbert Spaces Unlimited
- Regression and Least-Squares Classification Unlimited
- Support Vector Machines for Classification Unlimited
- Generalization Bounds, Introduction to Stability Unlimited
- Stability of Tikhonov Regularization Unlimited
- Consistency and Uniform Convergence Over Function Classes Unlimited
- Necessary and Sufficient Conditions for Uniform Convergence Unlimited
- Bagging and Boosting Unlimited
- Computer Vision, Object Detection Unlimited
- Approximation Theory Unlimited
- RKHS, Mercer Thm, Unbounded Domains, Frames and Wavelets Unlimited
- Bioinformatics Unlimited
- Text Summary Unlimited
- Regularization Networks Unlimited
- Morphable Models for Video Unlimited
- Leave-one-out Approximations Unlimited
- Bayesian Interpretations Unlimited
- Multiclass Classification Unlimited
- Math Camp 1: Functional Analysis Unlimited
- Math Camp 2: Lagrange Multipliers/Convex Optimization Unlimited
- SVM Rules of Thumb Unlimited
Instructor
