Reading List

Mathematics & Statistics

• Introduction to Probability — Blitzstein & Hwang
• The Elements of Statistical Learning — Hastie, Tibshirani & Friedman
• Deep Learning — Goodfellow, Bengio & Courville
• Stochastic Calculus for Finance II — Shreve

Information Theory

• A Mathematical Theory of Communication — Shannon (1948)
• Information Theory: A Tutorial Introduction — James Stone
• Information Theory, Inference, and Learning Algorithms — MacKay

Optimization & Control

• Dynamic Programming — Bellman (1957)
• Convex Optimization — Boyd & Vandenberghe

Probability & Inference

• Pattern Recognition and Machine Learning — Bishop
• Bayesian Data Analysis — Gelman et al.

Complex Systems & Emergence

• Flocks, Herds, and Schools: A Distributed Behavioral Model — Reynolds (1987)
• Introduction to Agent-Based Modeling — Santa Fe Institute
• Growing Artificial Societies — Epstein & Axtell

Systems

• Designing Data-Intensive Applications — Kleppmann

Finance & Markets

• Options, Futures and Other Derivatives — Hull
• Market Microstructure Theory — Maureen O'Hara
• Algorithmic and High-Frequency Trading — Cartea, Jaimungal & Penalva