Someday I hope to get around to reading all this stuff.

Programming

• The Fun of Programming by Jeremy Gibbons and Oege de Moor
• The books and papers of Simon Peyton Jones. (The Joy of Programming has a chapter about Composing Financial Contracts)
• Real World Haskell by Bryan O’Sullivan, Don Stewart, and John Goerzen

Machine Learning and AI

• Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran
• Artificial Intelligence: A Systems Approach by M. Tim Jones
• AI Application Programming by M. Tim Jones

Computer Architecture

• What Every Computer Scientist Should Know About Floating-Point Arithmetic by David Goldberg
• Hardware Synthesis From a Traditional Programming Language by Dylan Radcliffe
• The Structure and Performance of E?cient Interpreters by M. Anton Ertl and David Gregg
• Virtual Machine Showdown: Stack Versus Registers by Shi, Gregg, Beatty, and Ertl

Language Design

• The Definitive ANTLR Reference: Building Domain-Specific Languages by Terrence Parr
• Semantics of Programming Languages: Structures and Techniques by Carl A. Gunter
• *Formal Syntax and Semantics of Programming Languages: A Laboratory-Based Approach by Ken Slonneger
• Programming Languages: Concepts and Constructs by Ravi Sethi
• Programming Languages: Principles and Practice, Second Edition: Principles and Practice by Kenneth C. Louden
• Concepts of Programming Languages by Robert W. Sebesta
• The Essence ofML Type Inference by Pottier and Remy
• The Role of Programming in the Formulation of Ideas by Sussman and Wisdom
• The Use of Symbols in Mathematics and Logic by Sarukkai
• The Implementation of Lua 5.0 by Roberto Ierusalimschy, Luiz Henrique de Figueiredo, Waldemar Celes

Category Theory

• Physics, Topology, Logic and Computation: A Rosetta Stone. by John C. Baez and Mike Stay
• Categories for Software Engineering by José L. Fiadeiro
• Basic Category Theory for Computer Scientists by Benjamin C. Pierce
• A Gentle Introduction to Category Theory — the calculational approach by Maarten Fokkinga
• Notes on an Approach to Category Theory for Computer Scientists. by C.A.R. Hoare
• Category Theory for Computing Science. by M. Barr and C. Wells
• Categories, Types, and Structures. by A. Asperti and G. Longo

Algorithms and Data Structures

• Graph Theory Lessons

Economics and Human Behavior

• The Black Swan by Nassim Nicholas Taleb
• Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan
• Economics in One Lesson

Misc

• A Tutorial Introduction to the Minimum Description Length Principle by Peter Grunwald
• Erlang and Neural Networks
• Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl
• Stanford Computer Science Courses

The Singularity

• Nanosystems: Molecular Machinery, Manufacturing, and Computation by K. Eric Drexler
• Engines of creation by K. Eric Drexler
• The Singularity is Near by Ray Kurzweil
• Robot: Mere Machine to Transcendent Mind by Hans P. Moravec

Fiction

• Neuromancer by William Gibson
• The Ethics of Madness by Larry Niven
• Anathem by Neil Stephenson

Education

• How people learn by J. Bransford et al.
• The expert mind by P. Ross(Scientific Amperican pg 64, Aug 2006)
• The Cambridge Handbook of Expertize and Expert Performance by K. A. Ericsson, et al (Cambridge Univ. Press, 2006)
• Teaching Physics with the Physics Suite by E. Redish(Wiley, 2003)
• A new instrument for measuring student beliefs about physics and learing physics by Adams, Perkins, Podolefsky, Dubso, Finkelstein, Wieman (the Colorado Learning Attitudes about Science Survey, Physical Review Special Topics, Phys. Educ. Res. 2, 010101, 2006)
• Correlating Student Beliefs with Student Learning Using The Colorado Learning Attitudes about Science Survey (PERC Proceedings, 2004)