I am trying to read the following list of books on statistical learning. I have a BSCS and about 4 yrs of experience working in image processing and parallel programming. I won’t be an expert in the field by any means, however my aim is:
- not to be a script kiddie, using tools and algorithms without understanding the hows and whys.
- be able to read and digest the latest research in statistical learning, specially w.r.t computer vision.
Prerequisites I have studied in preparation:
- Matrix Algebra by James E. Gentle
- Statistical and Mathematical Methods lectures by Carlos Fernandez-Granda
Books to read:
- An Introduction to Statistical Learning with Applications in R by Robert Tibshirani et al.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Robert Tibshirani et al.
- Understanding Machine Learning From Theory To Algorithms by Shai Shalev-Shwartz et al.
- Pattern Recognition and Machine Learning by Christopher M. Bishops
- Information Theory, Inference, and Learning Algorithms by David J.C. MacKay
- Deep Learning by Ian Goodfellow et al.
- Convex Optimization by Stephen Boyd et al.
I am looking for a reading strategy. I'd specially appreciate input from users who’ve read the majority of the books.