1.Convex Optimization - Boyd and Vandenberghe

2.Probabilistic Robotics - Thrun, Burgard Fox

3.Pattern Recognition and Machine Learning - Bishop

4.The Emotion Machine - Marvin Minsky

1. Come And Take it: The Gun Printer's Guide to Free Thinking - Cody Wilson

6.Introduction to the Theory of Computation - Michael Sipser

7.Reinforcement Learning: An Introduction - Sutton & Barto

1. Advances in Minimum Description Length: Theory and Applications - Grünwald,Myung,Pitt

9.Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach - Barbara Hubbard and John H. Hubbard

10.Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach Solutions Manual

1. Multiple View Geometry in computer vision - Richard Hartley

12.Machine Learning: A Probabilistic Perspective - Kevin P. Murphy

1. Fundamentals of Computer Graphics - Marshner, Shirley

14.Probabilistic Graphical Models: Principles and Techniques - Daphne Koller

1. An introduction to Kolmogorov complexity and its applications - Ming Li

2. Concrete Mathematics - Donald Knuth,Patashnik, Graham

17.Probability Theory: The Logic of Science - Edwin Jaynes

18.Building Probabilistic Graphical Models with Python

19.Basic Category Theory for Computer Scientists

20.Logic of Provability

21.Causality - Judea Pearl

1. Art of Computer Programming (Vol 1,2,3,4A)- Don Knuth

23.Learning from Data: A Short Course -Hsuan-Tien Lin, Malik Magdon-Ismail, and Yaser Abu-Mostafa

1. Introduction to Algorithms- Cormen, Leiserson, Rivest, and Stein

EDIT : Was able to figure out a few more:

1. Information Theory, Inference And Learning Algorithms - David MacKay

26.Elements of Statistical Learning- Friedman,Tibshirani, and Hastie

27.Elements of Information Theory - Cover and Thomas

28.Signals And Systems - Alan V. Oppenheim

29.The Science of Radio - Paul J. Nahin

1. Gravity's Rainbow - Thomas Pynchon

2. The Minimum Description Length Principle - Peter D. Grünwald