Abhinav V. Sambasivan - Some Useful Resources

Online Resources

Here are a few links which I find very interesting and useful:

• Statistical Learning Theory course by Prof. Rob Nowak

• Convex Optimization course by Prof. Stephen Boyd

• Mitsuba - Physically Based Renderer, by Prof. Wenzel Jakob

• MIT OpenCourseWare - There are a lot of excellent courses here, my favorites being Linear Algebra by Prof. Gilbert Strang; and Signals and Systems, and Digital Signal Processing by Prof. Alan Oppenheim.

• Machine Learning course for beginners, by Andrew Ng (Course Era) - This is a nice place to start learning ML.

• Prof. Terence Tao’s Blog

Text Books

These are some textbooks which have helped me understand some crucial topics (for both my coursework and research):

Statistical Signal Processing and Compressive sensing

• Introduction to Nonparametric Estimation, by Alexandre B. Tsybokov

• A Rapid Introduction to Adaptive Filtering, by L. R. Vega, and H. Rey

• A Mathematical Introduction to Compressive Sensing, by Simon Foucart and Holger Rauhut

Statistical Learning and Machine Learning

• Pattern Recognition and Machine Learning, by Christopher M. Bishop

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Computer graphics: Physically based ray-tracing and rendering

• Physically Based Rendering: From Theory to Implementation, by Matt Pharr, Wenzel Jakob, and Greg Humphreys

Signal Processing

• Signals and Systems, by Alan V. Oppenheim, and Alan S. Willsky

• Discrete-Time Signal Processing, by Alan V. Oppenheim, and Ronald W. Schafer

Optimization

• Nonlinear Programming, by Dimitri P. Bertsekas

• Convex Optimization, by Stephen Boyd, and Lieven Vandenberghe

Other Useful Books

• Probability and Random Processes for Electrical and Computer Engineers, by John A. Gubner

• Linear Algebra and its Applications, by Gilbert Strang

• Introductory Functional Analysis with Applications, by Erwin Kreyzig

• The Matrix Cookbook, by Kaare Brandt Petersen, Michael Syskind Pedersen

• Introduction to Algorithms, by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein