Reading List

Mathematics for Machine Learning and Data Science

• Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
• Linear Algebra Done Right by Sheldon Axler
• Principles of Mathematical Analysis by Walter Rudin
• Convex Optimization by Stephen Boyd, Lieven Vandenberghe
• Introduction to Probability by Dimitri P. Bertsekas, John N. Tsitsiklis
• Stochastic Processes by Sheldon Ross
• Numerical Linear Algebra by Lloyd N. Trefethen, David Bau III
• Numerical Optimization by Jorge Nocedal, Stephen Wright
• Graph Theory by Reinhard Diestel
• Differential Equations with Boundary-Value Problems by Dennis G. Zill, Michael R. Cullen
• Complex Analysis by Lars Ahlfors

Probability and Statistics

• A First Course in Probability by Sheldon Ross
• Schaum's Outline of Probability and Statistics by John Schiller et al.
• Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang
• The Art of Probability by Richard W. Hamming
• Probability and Statistics: The Science of Uncertainty by Michael J. Evans and Jeffrey S. Rosenthal
• Statistics: Unlocking the Power of Data by Robin H. Lock, Patti Frazer Lock, and Trevor L. Gardner
• Problems in Probability by R.M. Dudley
• Introduction to the Theory of Statistics by Alexander M. Mood, Franklin A. Graybill, and Duane C. Boes
• Problems and Solutions in Probability and Statistics by M.K. Sen
• Probability and Statistics Problems and Solutions by M. J. S. Smith
• Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce
• The Probability and Statistics Companion by Christopher A. Pickles
• Statistics for Business and Economics by Paul Newbold et al.
• Exercises in Probability by Michel Loève
• Project Euler: Apply statistics and probability in programming challenges.
• Khan Academy: Interactive exercises and step-by-step solutions for probability and statistics.

Artificial Intelligence and Machine Learning

• Deep Learning with Python by François Chollet
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
• Python Machine Learning by Sebastian Raschka, Vahid Mirjalili
• Pattern Recognition and Machine Learning by Christopher M. Bishop
• Artificial Intelligence: A Modern Approach by Stuart Russell, Peter Norvig
• Probabilistic Machine Learning by Kevin P. Murphy
• Deep Reinforcement Learning Hands-On by Maxim Lapan
• Probabilistic Graphical Models by Daphne Koller, Nir Friedman
• Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto
• AI and Computational Neuroscience by Chris Eliasmith

Data Science and Analytics

• Python for Data Analysis by Wes McKinney
• Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce
• Storytelling with Data by Cole Nussbaumer Knaflic
• Data Science for Business by Foster Provost, Tom Fawcett
• Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• Designing Data-Intensive Applications by Martin Kleppmann
• Forecasting: Principles and Practice by Rob J. Hyndman, George Athanasopoulos
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Business Analytics and Strategy

• Competing on Analytics by Thomas H. Davenport, Jeanne G. Harris
• Predictive Analytics: The Power to Predict by Eric Siegel
• Data Smart by John W. Foreman
• Business Intelligence Guidebook by Rick Sherman
• Analytics at Work by Thomas H. Davenport, Jeanne G. Harris, Robert Morison

Finance and Investment

• Options, Futures, and Other Derivatives by John C. Hull
• Python for Finance by Yves Hilpisch
• Quantitative Finance for Dummies by Steve Bell
• Active Portfolio Management by Richard C. Grinold, Ronald N. Kahn
• Principles of Corporate Finance by Richard A. Brealey, Stewart C. Myers, Franklin Allen
• Valuation by Tim Koller, Marc Goedhart, David Wessels
• Financial Risk Management by Steve L. Allen
• Investment Valuation by Aswath Damodaran
• The Intelligent Investor by Benjamin Graham
• A Random Walk Down Wall Street by Burton G. Malkiel

Quantitative Finance

• Quantitative Equity Portfolio Management by Ludwig B. Chincarini, Daehwan Kim
• Paul Wilmott Introduces Quantitative Finance by Paul Wilmott
• The Concepts and Practice of Mathematical Finance by Mark S. Joshi
• Dynamic Asset Pricing Theory by Darrell Duffie
• Algorithmic and High-Frequency Trading by Alvaro Cartea, Sebastian Jaimungal, José Penalva

Corporate Finance and Banking

• Fundamentals of Corporate Finance by Stephen A. Ross, Randolph W. Westerfield, Bradford D. Jordan
• Corporate Finance and Governance by H. Kent Baker, Gerald S. Martin
• The Banking Industry Guidebook by Stephen V. Coffin
• Modern Banking by Shelagh Heffernan
• Asset-Liability Management in Banks by Wealthy Institute

Economics and Global Finance

• Economics: Principles and Policy by William J. Baumol, Alan S. Blinder
• Macroeconomics for Professionals by Leslie Lipschitz, Susan Schadler
• International Economics by Paul R. Krugman, Maurice Obstfeld, Marc J. Melitz
• Economic Growth by David N. Weil
• Principles of Microeconomics by N. Gregory Mankiw
• The Wealth of Nations by Adam Smith

Machine Learning Engineering and Systems Design

• Designing Machine Learning Systems by Chip Huyen
• Machine Learning Engineering by Andriy Burkov
• Building Machine Learning Powered Applications by Emmanuel Ameisen
• Reliable Machine Learning Systems by Nabil El Ioini, Kurt Vanmechelen
• Architecting Machine Learning Pipelines by Hiram Kümper

Biomedical and Healthcare Analytics

• Healthcare Analytics Made Simple by K. C. Santosh
• Data Analytics in Healthcare Research by Jianqing Fan, Runze Li
• Big Data in Healthcare by Katherine Marconi, Harold Lehmann
• Introduction to Health Economics by Guinness Lorna
• Bioinformatics Algorithms by Pavel A. Pevzner, Phillip Compeau
• Deep Learning in Medical Imaging by Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang

Investment Management and Quantitative Analysis

• Modern Portfolio Theory and Investment Analysis by Edwin J. Elton, Martin J. Gruber, Stephen J. Brown, William N. Goetzmann
• Quantitative Investment Analysis by CFA Institute
• Fixed Income Securities by Bruce Tuckman, Angel Serrat
• Machine Learning for Asset Managers by Marcos López de Prado
• Machine Learning in Finance by Matthew F. Dixon, Igor Halperin, Paul Bilokon

Additional Foundational Resources

• Numerical Recipes by William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery
• Time Series Analysis by James D. Hamilton
• Principles of Forecasting by J. Scott Armstrong
• Elements of Information Theory by Thomas M. Cover, Joy A. Thomas
• Statistical Learning Theory by Vladimir N. Vapnik