Foundations, Algorithms, and Applications
Jeremy Watt received his PhD in Computer Science and Electrical Engineering from Northwestern University, Illinois. His research interests lie in machine learning and computer vision, as well as numerical optimization. Reza Borhani received his PhD in Computer Science and Electrical Engineering from Northwestern University, Illinois. His research interests lie in the design and analysis of algorithms for problems in machine learning and computer vision. Aggelos K. Katsaggelos is a professor and holder of the AT&T chair in the Department of Electrical Engineering and Computer Science at Northwestern University, Illinois, where he also heads the Image and Video Processing Laboratory.
1. Introduction; Part I. The Basics: 2. Fundamentals of numerical optimization; 3. Knowledge-driven regression; 4. Knowledge-driven classification; Part II. Automatic Feature Design: 5. Automatic feature design for regression; 6. Automatic feature design for classification; 7. Kernels, backpropagation, and regularized cross-validation; Part III. Tools for Large Scale Data: 8. Advanced gradient schemes; 9. Dimension reduction techniques; Part IV. Appendices.