My reading list

  • QA278.2/Applied logistic regression 3e;Hosmer Lemeshow Sturdivant;Wiley-2013

Financial engineering

  • A Linear Algebra Primer for Financial Engineering (Stefanica, 2014, 9780979757655)
  • HG106/Stochastic calculus for finance. Vol I The binomial asset pricing model;Shreve;Springer-2004
  • HG106/Stochastic calculus for finance. Vol II Continuous time models;Shreve;Springer-2004
  • HG173/Paul Wilmott introduces quantitative finance 2e;Wilmott;Wiley-2007
  • HG176.7/A primer for the mathematics of financial engineering;Stefanica;FE-2008
  • HG176.7/Monte Carlo methods in financial engineering;Glasserman;Springer-2004
  • HG176.7/Statistics and data analysis for financial engineering. With R examples 2e;Ruppert Matteson;Springer-2015
  • HG6024.A3/An introduction to the mathematics of financial derivatives 3e;Hirsa Neftci;Academic-2014
  • HG6024.A3/Arbitrage theory in continuous time;Bjork;Oxford-1998
  • HG6024.A3/C++ design patterns and derivatives pricing 2e;Joshi;Cambridge-2008
  • HG6024.A3/Financial calculus. An introduction to derivative pricing;Baxter Rennie;Cambridge-1996
  • HG6024.A3/Options, futures, and other derivatives 8e;Hull;PH-2012
  • HG6024.A3/The concepts and practice of mathematical finance 2e;Joshi;Cambridge-2008
  • HG6024/More mathematical finance;Joshi;PW-2011

Systematic trading

  • HF5470/Trading and exchanges. Market microstructure for practitioners;Harris;Oxford-2003
  • HG4515.15/Quantitative value. A practitioner’s guide to automating intelligent investment and eliminating behavioral errors + website;Carlisle;Wiley-2013
  • HG4515.5/Algorithmic trading & DMA. An introduction to direct access trading strategies;Johnson;4Myeloma-2010
  • HG4515.5/Finding alphas. A quantitative approach to building trading strategies;Tulchinsky;Wiley-2015
  • HG4515.95/Algorithmic and high-frequency trading;Cartea;Cambridge-2015
  • HG4515.95/Machine trading. Deploying computer algorithms to conquer the markets;Chan;Wiley-2017
  • HG4521/Dual momentum investing. An innovative strategy for higher returns with lower risk;Antonacci;MGH-2015
  • HG4521/Systematic trading. A unique new method for designing trading and investing systems;Carver;HH-2015
  • HG4529.5/Inside the black box. A simple guide to quantitative and high-frequency trading 2e;Narang;Wiley-2013
  • HG4529/Algorithmic trading. Winning strategies and their rationale;Chan;Wiley-2013
  • HG4529/Quantitative trading. How to build your own algorithmic trading business;Chan;Wiley-2009
  • HG4661/Quantitative momentum. A practitioner’s guide to building a momentum-based stock selection system;Gray Vogel;Wiley-2016
  • HG6024.A3/Following the trend. Diversified managed futures trading;Clenow;Wiley-2013
  • HG6024.A3/Volatility trading 2e;Sinclair;Wiley-2013
  • Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies

Machine learning and deep learning

  • Q325.5/Deep learning;Goodfellow Bengio Courville;MIT-2016
  • Q325.5/Hands-on machine learning with Scikit-Learn and TensorFlow. Concepts, tools, and techniques to build intelligent systems 1e;Geron;OReilly-2017
  • Q325.5/Machine learning. A probabilistic perspective;Murphy;MIT-2012
  • Q325.75/The elements of statistical learning. Data mining, inference, and prediction 2e;Hastie Tibshirani Friedman;Springer-2009
  • Q327/Pattern recognition and machine learning;Bishop;Springer-2006
  • Q375/Probabilistic graphical models. Principles and applications;Sucar;Springer-2015
  • QA267/Bayesian reasoning and machine learning;Barber;Cambridge-2011
  • QA276/An introduction to statistical learning. With applications in R;James;Springer-2013
  • QA276/Applied predictive modeling;Kuhn Johnson;Springer-2013
  • QA76.73.P98/Python machine learning. Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2e;Raschka Mirajalili;Packt-2017
  • bookpile/Reinforcement Learning. An Introduction 2e;Sutton Barto;MIT-2018

Cambridge Undergraduate Math

Studying mathematics

  • How to study for a maths degree, Lara Alcock (OUP, 2013)

Historical and general

  • A Mathematician’s Apology, G.H. Hardy (CUP, 1992)
  • A Russian Childhood, S. Kovalevskaya (trans. B. Stillman) (Springer, 1978, now out of print)
  • Alan Turing, the Enigma, A. Hodges (Vintage, 1992)
  • J. McLeish Number (Bloomsbury, 1991)
  • Littlewood’s Miscellany (edited by B. Bollobas) (CUP, 1986)
  • QA174.7.S96/Finding moonshine. A mathematician’s journey through symmetry;Sautoy;FE-2008
  • QA21/Makers of mathematics;Hollingdale;Penguin-1989
  • QA244/Fermat’s last theorem. The story of a riddle that confounded the world’s greatest minds for 358 years;Singh;FE-1997
  • QA246/The music of the primes. Searching to solve the greatest mystery in mathematics 1e;Sautoy;HarperCollins-2003
  • QA29.E68/The man who loved only numbers. The story of Paul Erdos and the search for mathematical truth 1e;Hoffman;Hyperion-1998
  • QA29.R3/The man who knew infinity. A life of the genius Ramanujan;Kanigel;WS-1991
  • Surely You’re Joking, Mr Feynman R.P. Feynman (Arrow Books, 1992)


  • QA95/The colossal book of mathematics. Classic puzzles, paradoxes, and problems-Number theory, algebra, geometry, probability, topology, game theory, infinity, and other topics of recreational mathematics 1e;Gardner;Norton-2001
  • QA95/Game, set, and math. Enigmas and conundrums;Stewart;BB-1989
  • To Infinity and Beyond Eli Maor (Princeton, 1991)
  • A Mathematical Mosaic Ravi Vakil (Mathematical Association of America, 1997)

Readable mathematics

  • Archimedes’ Revenge P. Hoffman (Penguin, 1991)
  • Beyond Numeracy J. A. Paulos (Penguin, 1991)
  • Calculus for the Ambitious T.W. K¨orner (CUP, 2014)
  • Fractals. Images of Chaos H. Lauwerier (Penguin, 1991)
  • Mathematics: a very short introduction Timothy Gowers (CUP, 2002)
  • New Applications of Mathematics C. Bondi (ed.) (Penguin, 1991)
  • Q172.5.C45/Chaos. Making a new science 20th anniversary edition;Gleick;Penguin-2008
  • QA13/How to think like a mathematician. A companion to undergraduate mathematics;Houston;Cambridge-2009
  • QA21/The math book. From Pythagoras to the 57th dimension, 250 milestones in the history of mathematics;Pickover;Sterling-2009
  • QA300/Solving mathematical problems. A personal perspective;Tao;Oxford-2006
  • QA37.2/What is mathematics?. An elementary approach to ideas and methods 2e;Courant Robbins Stewart;Oxford-1996
  • QA8.4/The mathematical experience. Study edition;Davis Hersh Marchisotto;Birkhauser-2012
  • QA93/The pleasures of counting;Korner;Cambridge-1996
  • Reaching for Infinity S. Gibilisco (Tab/McGraw-Hill, 1990)
  • The New Scientist Guide to Chaos N. Hall (ed.) (Penguin, 1991)
  • The Penguin Dictionary of Curious and Interesting Numbers D. Wells (Penguin, 1997)
  • What’s Happening in the Mathematical Sciences B. Cipra (AMS, 1993, ’94, ’96, ’99, ’02)

Readable theoretical physics

  • QC173.6/Was Einstein right?. Putting general relativity to the test;Will;Basic-1986
  • QC174.12/The new quantum universe;Hey Walters;Cambridge-2003
  • QC7/Hidden unity in nature’s laws;Taylor;Cambridge-2001
  • QC793.5.P422/QED. The strange theory of light and matter;Feynman;Princeton-1985
  • The Accidental Universe P.C.W. Davies (CUP, 1982)
  • The Cosmic Onion Frank Close (Heinemann, 1983)

Readable textbooks

  • A Concise Introduction to Pure Mathematics Martin Liebeck (Chapman& Hall/CRC Mathematics)
  • A First Course in Mechanics Mary Lunn (OUP, 1991)
  • Advanced Problems in Mathematics S.T.C. Siklos (1996 and 2003)
  • Groups: A Path to Geometry R.P. Burn (CUP, 1987)
  • Mathematical Methods for Science Students G. Stephenson (Longman, 1973)
  • QA273.25/Schaum’s outline of theory and problems of probability and statistics;Spiegel;MGH-1975
  • QA401/Mathematical methods for physics and engineering. A comprehensive guide;Riley Hobson Bence;Cambridge-1997
  • QA76.9.A43/Algorithmics. The spirit of computing 3e;Harel Feldman;AW-2004
  • What is Mathematical Analysis? John Baylis (MacMillan, 1991)
  • Yet Another Introduction to Analysis V. Bryant (CUP, 1990)

10 must-read books for machine learning and data science

  • Machine Learning Yearning By Andrew Ng
  • Q325.5/Deep learning;Goodfellow Bengio Courville;MIT-2016
  • Q325.5/Understanding machine learning. From foundations to algorithms;Shalev-Shwartz Jerusalem Ben-David Canada;Cambridge-2014
  • Q325.75/The elements of statistical learning. Data mining, inference, and prediction 2e;Hastie Tibshirani Friedman;Springer-2009
  • QA276.4/Think stats 2e;Downey;OReilly-2015
  • QA276/An introduction to statistical learning. With applications in R;James;Springer-2013
  • QA76.9.A25/Bayesian methods for hackers. Probabilistic programming and bayesian inference;Davidson-Pilon;AW-2016
  • QA76.9.D343/Mining of massive datasets;Rajaraman Ullman;Cambridge-2012
  • bookpile/A Programmer’s Guide to Data Mining;Zacharski;
  • bookpile/Foundations of Data Science;Blum Hopcroft Kannan;2018

Machine learning books

Popular Science Machine Learning Books

  • CB158/The signal and the noise. Why so many predictions fail–but some don’t;Silver;Penguin-2012 [recommended]
  • H61.4/Predictive analytics. The power to predict who will click, buy, lie, or die;Siegel;Wiley-2013
  • Q387/The master algorithm. How the quest for the ultimate learning machine will remake our world;Domingos;Basic-2015
  • QA273/The Drunkard’s walk. How randomness rules our lives 1e;Mlodinow;Pantheon-2008
  • QA276/Naked statistics. Stripping the dread from the data 1e;Wheelan;WWNC-2013
  • QA76.9.B45/Weapons of math destruction. How big data increases inequality and threatens democracy 1e;O’Neil;Crown-2016

Beginner Machine Learning Books

  • QA76.9.D343/Data science for business. What you need to know about data mining and data-analytic thinking 1e;Provost Fawcett;OReilly-2013
  • QA76.9.D343/Data smart. Using data science to transform information into insight;Foreman;Wiley-2014
  • QA76.9.D343/Data mining. Practical machine learning tools and techniques 3e;Witten Frank Hall;MK-2011 [recommended]
  • QA76.9.D343/Doing data science 1e;Schutt O’Neil;OReilly-2013

Introductory Machine Learning Books

  • Q325.5/Machine learning in action;Harrington;Manning-2012
  • QA276/An introduction to statistical learning. With applications in R;James;Springer-2013 [recommended]
  • QA276/Applied predictive modeling;Kuhn Johnson;Springer-2013
  • QA76.9.A43/Machine learning for hackers 1e;Conway White;OReilly-2012
  • T58.5/Programming collective intelligence. Building smart web 2.0 applications;Segaran;OReilly-2007

Machine Learning Textbooks

  • Q325.75/The elements of statistical learning. Data mining, inference, and prediction 2e;Hastie Tibshirani Friedman;Springer-2009 [recommended]
  • Q327/Pattern recognition and machine learning;Bishop;Springer-2006
  • Q325.5/Learning from data. A short course;Abu-Mostafa Magdon-Ismail Lin;AC-2012
  • Q325.5/Machine learning. A probabilistic perspective;Murphy;MIT-2012
  • Q325.5/Machine Learning;Mitchell;MGH-1997
  • Q325.5/Machine learning. The art and science of algorithms that make sense of data;Flach;Cambridge-2012
  • Q325.5/Foundations of machine learning;Mohri Rostamizadeh Talwalkar;MIT-2012

Machine Learning With R

  • Q325.5/Mastering machine learning with R. Master machine learning techniques with R to deliver insights for complex projects;Lesmeister;Packt-2015
  • Q325.5/R machine learning essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment;Usuelli;Packt-2014
  • QA276.45.R3/Machine learning with R cookbook. Explore over 110 recipes to analyze data and build predictive models with the simple and easy-to-use R code;Chiu;Packt-2015
  • QA276.45.R3/Practical data science with R;Zumel Mount;Manning-2014
  • QA276/An introduction to statistical learning. With applications in R;James;Springer-2013
  • QA276/Applied predictive modeling;Kuhn Johnson;Springer-2013 [recommended]
  • QA76.73.R3/R machine learning by example. Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully;Bali Sarkar;Packt-2016
  • QA76.9.A25/Machine learning with R. Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications;Lantz;Packt-2013
  • QA76.9.D343/R and data mining. Examples and case studies;Zhao;Academic-2013
  • QA76/R for data science. Import, tidy, transform, visualize, and model data 1e;Wickham Grolemund;OReilly-2017

Machine Learning With Python

  • Q325.5/Hands-on machine learning with Scikit-Learn and TensorFlow. Concepts, tools, and techniques to build intelligent systems 1e;Geron;OReilly-2017
  • Q325.5/Machine Learning in Python. Essential techniques for predictive analysis;Bowles;Wiley-2015
  • Q325.5/Real-world machine learning;Brink Richards Fetherolf;Manning-2017
  • QA76.73.P98/Data science from scratch 1e;Grus;OReilly-2015
  • QA76.73.P98/Introduction to machine learning with Python. A guide for data scientists 1e;Muller Guido;OReilly-2017
  • QA76.73.P98/Python data science handbook. Essential tools for working with data 1e;Vanderplas;OReilly-2017
  • QA76.73.P98/Python machine learning. Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2e;Raschka Mirajalili;Packt-2017 [recommended]
  • QA76.9.D343/Introducing data science. Big data, machine learning, and more, using Python tools;Cielen Meysman Ali;Manning-2016
  • Vital Introduction to Machine Learning with Python: Best Practices to Improve and Optimize Machine Learning Systems and Algorithms

Deep Learning

  • Q325.5/Deep learning;Goodfellow Bengio Courville;MIT-2016 [recommended]
  • Q325.5/Getting started with tensorflow. Get up and running with the latest numerical computing library by Google and dive deeper into your data!;Zaccone;Packt-2016
  • Q325.5/Learning TensorFlow. A guide to building deep learning systems 1e;Hope;OReilly-2017
  • Q325.5/Machine learning with TensorFlow;Shukla Fricklas;Manning-2018
  • Q325.5/TensorFlow for machine intelligence. A hands-on introduction to learning algorithms;Abrahams Hafner Erwitt Scarpinelli;BE-2016
  • Q325.5/TensorFlow machine learning cookbook;McClure;Packt-2017
  • TA347.A78/Deep learning. A practitioner’s approach 1e;Patterson Gibson;OReilly-2017
  • TA347.A78/Fundamentals of deep learning. Designing next-generation machine intelligence algorithms 1e;Buduma Locascio;OReilly

Time Series Forecasting

  • Forecasting: principles and practice, by Rob J Hyndman and George Athanasopoulos, 2013 [introductory]
  • QA280/Introduction to time series and forecasting 2e;Brockwell Davis;Springer-2002
  • QA280/Time series analysis. Forecasting and control 5e;Box Jenkins Reinsel Ljung;Wiley-2016 [recommended]
  • bookpile/Practical Time Series Forecasting. A Hands-On Guide 2e (9780997847932);Shmueli;AS-2011

AIML book list

  • Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs) by Bradley Efron
  • Asymptotic Statistics (Cambridge Series in Statistical and Probabilistic Mathematics) by A. W. van der Vaart
  • Introductory Lectures on Convex Optimization: A Basic Course (Applied Optimization) by Y. Nesterov
  • Introduction to Nonparametric Estimation (Springer Series in Statistics) by Alexandre B. Tsybakov
  • Introductory Functional Analysis with Applications by Erwin Kreyszig
  • Elements of Information Theory 2nd Edition (Wiley Series in Telecommunications and Signal Processing) by Thomas M. Cover, Joy A. Thomas
  • Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition) by Gene H. Golub, Van Loan, Charles F.
  • Introduction to Linear Optimization (Athena Scientific Series in Optimization and Neural Computation, 6) by Dimitris Bertsimas, John N. Tsitsiklis
  • Convex Optimization, With Corrections 2008 by Stephen Boyd, Lieven Vandenberghe
  • Probability: Theory and Examples (Probability: Theory & Examples) by Richard Durrett
  • A User’s Guide to Measure Theoretic Probability (Cambridge Series in Statistical and Probabilistic Mathematics) by David Pollard
  • Probability and Random Processes by Geoffrey R. Grimmett, David R. Stirzaker
  • Monte Carlo Statistical Methods (Springer Texts in Statistics) by Christian P. Robert, George Casella
  • Elements of Large-Sample Theory (Springer Texts in Statistics) by E.L. Lehmann
  • A Course in Large Sample Theory (Chapman & Hall/CRC Texts in Statistical Science) by Thomas S. Ferguson
  • Statistical Inference by George Casella, Roger L. Berger
  • Mathematical Statistics and Data Analysis by John A. Rice
  • All of Statistics: A Concise Course in Statistics by Larry Wasserman
  • Pattern Recognition and Machine Learning by Christopher M. Bishop
  • The Elements of Statistical Learning by T. Hastie et al
  • Information Theory, Inference, and Learning Algorithms, David McKay
  • Introduction to Information Retrieval - Manning et al.
  • The Algorithm Design Manual, 2nd Edition - Steven Skiena

  • Casella, G. and Berger, R.L. (2001). “Statistical Inference” Duxbury Press.
  • Ferguson, T. (1996). “A Course in Large Sample Theory” Chapman & Hall/CRC.
    For a slightly more advanced book that’s quite clear on mathematical techniques, the following book is quite good:
  • Lehmann, E. (2004). “Elements of Large-Sample Theory” Springer.
    You’ll need to learn something about asymptotics at some point, and a good starting place is:
  • Gelman, A. et al. (2003). “Bayesian Data Analysis” Chapman & Hall/CRC.
    Those are all frequentist books. You should also read something Bayesian:
  • Robert, C. and Casella, G. (2005). “Monte Carlo Statistical Methods” Springer.
    and you should start to read about Bayesian computation:
  • Grimmett, G. and Stirzaker, D. (2001). “Probability and Random Processes” Oxford.
    On the probability front, a good intermediate text is:
  • Pollard, D. (2001). “A User’s Guide to Measure Theoretic Probability” Cambridge.
    At a more advanced level, a very good text is the following:
  • The standard advanced textbook is Durrett, R. (2005). “Probability: Theory and Examples” Duxbury.
  • Bertsimas, D. and Tsitsiklis, J. (1997). “Introduction to Linear Optimization” Athena.
    Machine learning research also reposes on optimization theory. A good starting book on linear optimization that will prepare you for convex optimization:
  • Boyd, S. and Vandenberghe, L. (2004). “Convex Optimization” Cambridge.
  • Golub, G., and Van Loan, C. (1996). “Matrix Computations” Johns Hopkins.
    Getting a full understanding of algorithmic linear algebra is also important. At some point you should feel familiar with most of the material in
  • Cover, T. and Thomas, J. “Elements of Information Theory” Wiley.
    It’s good to know some information theory. The classic is:

Proof Technique

  • Velleman’s “How to Prove It”
  • Gries and Schneider’s “A Logical Approach to Discrete Math”


  • Calculus (best “lite” book - Calculus by Strang (free download), best “heavy” books - (d) Calculus by Spivak, (e) Principles of Mathematical Analysis a.k.a “Baby Rudin”)
  • Discrete Math (ALADM above + (g) a good book on Algorithms, Cormen will do - though working through it comprehensively is … hard!
  • Linear Algebra (First work through Strang’s book, then (i) Axler’s)
  • Probability (see Bradford’s very comprehensive recommendations) and
  • Statistics (I would reccomend Devore and Peck for the total beginner but it is a damn expensive book. So hit a library or get a bootlegged copy to see if it suits you before buying a copy, see brad’s list for advanced stuff.)
  • Information Theory (MacKay’s book is freely available online)

Basic AI

  • AIMA 3d Edition (I prefer this to Mitchell)

Machine Learning

  • “Pattern Recognition and Machine Learning” by Christopher Bishop,
  • Elements of Statistical Learning” (free download).
  • Neural Network Design by Hagan Demuth and Kneale,
  • Neural Networks, A Comprehensive Foundation (2nd edition) - By Haykin (there is a newer edition out but I don’t know anything about that, this is the one I used)
  • Neural Networks for Pattern Recognition ( Bishop).

At this point you are in good shape to read any papers in NN. My reccomendations - anything by Yann LeCun and Geoffrey Hinton. Both do amazing research. Reinforcement Learning

  • Reinforcement Learning - An Introduction by Barto and Sutton (follow up with “Recent Advances In reinforcement Learning” (PDF) which is an old paper but a GREAT introduction to HierarchicalReinforcement learning)
  • Neuro Dynamic Programming by Bertsekas

Computer Vision

  • Introductory Techniques for 3-D Computer Vision, by Emanuele Trucco and Alessandro Verri.
  • An Invitation to 3-D Vision by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry. (warning TOUGH!!)


  • Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) - not about robotics per se but useful to understand the next book
  • Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) by Thrun, Burgard and Fox