My reading list
 QA278.2/Applied logistic regression 3e;Hosmer Lemeshow Sturdivant;Wiley2013
Financial engineering
https://www.quantstart.com/articles/QuantitativeFinanceReadingList
 A Linear Algebra Primer for Financial Engineering (Stefanica, 2014, 9780979757655)
 HG106/Stochastic calculus for finance. Vol I The binomial asset pricing model;Shreve;Springer2004
 HG106/Stochastic calculus for finance. Vol II Continuous time models;Shreve;Springer2004
 HG173/Paul Wilmott introduces quantitative finance 2e;Wilmott;Wiley2007
 HG176.7/A primer for the mathematics of financial engineering;Stefanica;FE2008
 HG176.7/Monte Carlo methods in financial engineering;Glasserman;Springer2004
 HG176.7/Statistics and data analysis for financial engineering. With R examples 2e;Ruppert Matteson;Springer2015
 HG6024.A3/An introduction to the mathematics of financial derivatives 3e;Hirsa Neftci;Academic2014
 HG6024.A3/Arbitrage theory in continuous time;Bjork;Oxford1998
 HG6024.A3/C++ design patterns and derivatives pricing 2e;Joshi;Cambridge2008
 HG6024.A3/Financial calculus. An introduction to derivative pricing;Baxter Rennie;Cambridge1996
 HG6024.A3/Options, futures, and other derivatives 8e;Hull;PH2012
 HG6024.A3/The concepts and practice of mathematical finance 2e;Joshi;Cambridge2008
 HG6024/More mathematical finance;Joshi;PW2011
Systematic trading
https://www.quantstart.com/articles/QuantitativeFinanceReadingList
 HF5470/Trading and exchanges. Market microstructure for practitioners;Harris;Oxford2003
 HG4515.15/Quantitative value. A practitioner’s guide to automating intelligent investment and eliminating behavioral errors + website;Carlisle;Wiley2013
 HG4515.5/Algorithmic trading & DMA. An introduction to direct access trading strategies;Johnson;4Myeloma2010
 HG4515.5/Finding alphas. A quantitative approach to building trading strategies;Tulchinsky;Wiley2015
 HG4515.95/Algorithmic and highfrequency trading;Cartea;Cambridge2015
 HG4515.95/Machine trading. Deploying computer algorithms to conquer the markets;Chan;Wiley2017
 HG4521/Dual momentum investing. An innovative strategy for higher returns with lower risk;Antonacci;MGH2015
 HG4521/Systematic trading. A unique new method for designing trading and investing systems;Carver;HH2015
 HG4529.5/Inside the black box. A simple guide to quantitative and highfrequency trading 2e;Narang;Wiley2013
 HG4529/Algorithmic trading. Winning strategies and their rationale;Chan;Wiley2013
 HG4529/Quantitative trading. How to build your own algorithmic trading business;Chan;Wiley2009
 HG4661/Quantitative momentum. A practitioner’s guide to building a momentumbased stock selection system;Gray Vogel;Wiley2016
 HG6024.A3/Following the trend. Diversified managed futures trading;Clenow;Wiley2013
 HG6024.A3/Volatility trading 2e;Sinclair;Wiley2013
 Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies
Machine learning and deep learning
 Q325.5/Deep learning;Goodfellow Bengio Courville;MIT2016
 Q325.5/Handson machine learning with ScikitLearn and TensorFlow. Concepts, tools, and techniques to build intelligent systems 1e;Geron;OReilly2017
 Q325.5/Machine learning. A probabilistic perspective;Murphy;MIT2012
 Q325.75/The elements of statistical learning. Data mining, inference, and prediction 2e;Hastie Tibshirani Friedman;Springer2009
 Q327/Pattern recognition and machine learning;Bishop;Springer2006
 Q375/Probabilistic graphical models. Principles and applications;Sucar;Springer2015
 QA267/Bayesian reasoning and machine learning;Barber;Cambridge2011
 QA276/An introduction to statistical learning. With applications in R;James;Springer2013
 QA276/Applied predictive modeling;Kuhn Johnson;Springer2013
 QA76.73.P98/Python machine learning. Machine learning and deep learning with Python, scikitlearn, and TensorFlow 2e;Raschka Mirajalili;Packt2017
 bookpile/Reinforcement Learning. An Introduction 2e;Sutton Barto;MIT2018
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;FE2008
 QA21/Makers of mathematics;Hollingdale;Penguin1989
 QA244/Fermat’s last theorem. The story of a riddle that confounded the world’s greatest minds for 358 years;Singh;FE1997
 QA246/The music of the primes. Searching to solve the greatest mystery in mathematics 1e;Sautoy;HarperCollins2003
 QA29.E68/The man who loved only numbers. The story of Paul Erdos and the search for mathematical truth 1e;Hoffman;Hyperion1998
 QA29.R3/The man who knew infinity. A life of the genius Ramanujan;Kanigel;WS1991
 Surely You’re Joking, Mr Feynman R.P. Feynman (Arrow Books, 1992)
Recreational
 QA95/The colossal book of mathematics. Classic puzzles, paradoxes, and problemsNumber theory, algebra, geometry, probability, topology, game theory, infinity, and other topics of recreational mathematics 1e;Gardner;Norton2001
 QA95/Game, set, and math. Enigmas and conundrums;Stewart;BB1989
 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;Penguin2008
 QA13/How to think like a mathematician. A companion to undergraduate mathematics;Houston;Cambridge2009
 QA21/The math book. From Pythagoras to the 57th dimension, 250 milestones in the history of mathematics;Pickover;Sterling2009
 QA300/Solving mathematical problems. A personal perspective;Tao;Oxford2006
 QA37.2/What is mathematics?. An elementary approach to ideas and methods 2e;Courant Robbins Stewart;Oxford1996
 QA8.4/The mathematical experience. Study edition;Davis Hersh Marchisotto;Birkhauser2012
 QA93/The pleasures of counting;Korner;Cambridge1996
 Reaching for Infinity S. Gibilisco (Tab/McGrawHill, 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;Basic1986
 QC174.12/The new quantum universe;Hey Walters;Cambridge2003
 QC7/Hidden unity in nature’s laws;Taylor;Cambridge2001
 QC793.5.P422/QED. The strange theory of light and matter;Feynman;Princeton1985
 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;MGH1975
 QA401/Mathematical methods for physics and engineering. A comprehensive guide;Riley Hobson Bence;Cambridge1997
 QA76.9.A43/Algorithmics. The spirit of computing 3e;Harel Feldman;AW2004
 What is Mathematical Analysis? John Baylis (MacMillan, 1991)
 Yet Another Introduction to Analysis V. Bryant (CUP, 1990)
10 mustread books for machine learning and data science
https://www.kdnuggets.com/2017/04/10freemustreadbooksmachinelearningdatascience.html
 Machine Learning Yearning By Andrew Ng
 Q325.5/Deep learning;Goodfellow Bengio Courville;MIT2016
 Q325.5/Understanding machine learning. From foundations to algorithms;ShalevShwartz Jerusalem BenDavid Canada;Cambridge2014
 Q325.75/The elements of statistical learning. Data mining, inference, and prediction 2e;Hastie Tibshirani Friedman;Springer2009
 QA276.4/Think stats 2e;Downey;OReilly2015
 QA276/An introduction to statistical learning. With applications in R;James;Springer2013
 QA76.9.A25/Bayesian methods for hackers. Probabilistic programming and bayesian inference;DavidsonPilon;AW2016
 QA76.9.D343/Mining of massive datasets;Rajaraman Ullman;Cambridge2012
 bookpile/A Programmer’s Guide to Data Mining;Zacharski;
 bookpile/Foundations of Data Science;Blum Hopcroft Kannan;2018
Machine learning books
https://machinelearningmastery.com/machinelearningbooks/
Popular Science Machine Learning Books
 CB158/The signal and the noise. Why so many predictions fail–but some don’t;Silver;Penguin2012 [recommended]
 H61.4/Predictive analytics. The power to predict who will click, buy, lie, or die;Siegel;Wiley2013
 Q387/The master algorithm. How the quest for the ultimate learning machine will remake our world;Domingos;Basic2015
 QA273/The Drunkard’s walk. How randomness rules our lives 1e;Mlodinow;Pantheon2008
 QA276/Naked statistics. Stripping the dread from the data 1e;Wheelan;WWNC2013
 QA76.9.B45/Weapons of math destruction. How big data increases inequality and threatens democracy 1e;O’Neil;Crown2016
Beginner Machine Learning Books
 QA76.9.D343/Data science for business. What you need to know about data mining and dataanalytic thinking 1e;Provost Fawcett;OReilly2013
 QA76.9.D343/Data smart. Using data science to transform information into insight;Foreman;Wiley2014
 QA76.9.D343/Data mining. Practical machine learning tools and techniques 3e;Witten Frank Hall;MK2011 [recommended]
 QA76.9.D343/Doing data science 1e;Schutt O’Neil;OReilly2013
Introductory Machine Learning Books
 Q325.5/Machine learning in action;Harrington;Manning2012
 QA276/An introduction to statistical learning. With applications in R;James;Springer2013 [recommended]
 QA276/Applied predictive modeling;Kuhn Johnson;Springer2013
 QA76.9.A43/Machine learning for hackers 1e;Conway White;OReilly2012
 T58.5/Programming collective intelligence. Building smart web 2.0 applications;Segaran;OReilly2007
Machine Learning Textbooks
 Q325.75/The elements of statistical learning. Data mining, inference, and prediction 2e;Hastie Tibshirani Friedman;Springer2009 [recommended]
 Q327/Pattern recognition and machine learning;Bishop;Springer2006
 Q325.5/Learning from data. A short course;AbuMostafa MagdonIsmail Lin;AC2012
 Q325.5/Machine learning. A probabilistic perspective;Murphy;MIT2012
 Q325.5/Machine Learning;Mitchell;MGH1997
 Q325.5/Machine learning. The art and science of algorithms that make sense of data;Flach;Cambridge2012
 Q325.5/Foundations of machine learning;Mohri Rostamizadeh Talwalkar;MIT2012
Machine Learning With R
 Q325.5/Mastering machine learning with R. Master machine learning techniques with R to deliver insights for complex projects;Lesmeister;Packt2015
 Q325.5/R machine learning essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment;Usuelli;Packt2014
 QA276.45.R3/Machine learning with R cookbook. Explore over 110 recipes to analyze data and build predictive models with the simple and easytouse R code;Chiu;Packt2015
 QA276.45.R3/Practical data science with R;Zumel Mount;Manning2014
 QA276/An introduction to statistical learning. With applications in R;James;Springer2013
 QA276/Applied predictive modeling;Kuhn Johnson;Springer2013 [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 realworld problems successfully;Bali Sarkar;Packt2016
 QA76.9.A25/Machine learning with R. Learn how to use R to apply powerful machine learning methods and gain an insight into realworld applications;Lantz;Packt2013
 QA76.9.D343/R and data mining. Examples and case studies;Zhao;Academic2013
 QA76/R for data science. Import, tidy, transform, visualize, and model data 1e;Wickham Grolemund;OReilly2017
Machine Learning With Python
 Q325.5/Handson machine learning with ScikitLearn and TensorFlow. Concepts, tools, and techniques to build intelligent systems 1e;Geron;OReilly2017
 Q325.5/Machine Learning in Python. Essential techniques for predictive analysis;Bowles;Wiley2015
 Q325.5/Realworld machine learning;Brink Richards Fetherolf;Manning2017
 QA76.73.P98/Data science from scratch 1e;Grus;OReilly2015
 QA76.73.P98/Introduction to machine learning with Python. A guide for data scientists 1e;Muller Guido;OReilly2017
 QA76.73.P98/Python data science handbook. Essential tools for working with data 1e;Vanderplas;OReilly2017
 QA76.73.P98/Python machine learning. Machine learning and deep learning with Python, scikitlearn, and TensorFlow 2e;Raschka Mirajalili;Packt2017 [recommended]
 QA76.9.D343/Introducing data science. Big data, machine learning, and more, using Python tools;Cielen Meysman Ali;Manning2016
 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;MIT2016 [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;Packt2016
 Q325.5/Learning TensorFlow. A guide to building deep learning systems 1e;Hope;OReilly2017
 Q325.5/Machine learning with TensorFlow;Shukla Fricklas;Manning2018
 Q325.5/TensorFlow for machine intelligence. A handson introduction to learning algorithms;Abrahams Hafner Erwitt Scarpinelli;BE2016
 Q325.5/TensorFlow machine learning cookbook;McClure;Packt2017
 TA347.A78/Deep learning. A practitioner’s approach 1e;Patterson Gibson;OReilly2017
 TA347.A78/Fundamentals of deep learning. Designing nextgeneration 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;Springer2002
 QA280/Time series analysis. Forecasting and control 5e;Box Jenkins Reinsel Ljung;Wiley2016 [recommended]
 bookpile/Practical Time Series Forecasting. A HandsOn Guide 2e (9780997847932);Shmueli;AS2011
AIML book list
 LargeScale 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 LargeSample 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 http://wwwstat.stanford.edu/~tibs/ElemStatLearn/
 Information Theory, Inference, and Learning Algorithms, David McKay http://www.inference.phy.cam.ac.uk/itprnn/book.html
 Introduction to Information Retrieval  Manning et al. http://nlp.stanford.edu/IRbook/informationretrievalbook.html

The Algorithm Design Manual, 2nd Edition  Steven Skiena http://www.algorist.com/
 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 LargeSample 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”
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 3D Computer Vision, by Emanuele Trucco and Alessandro Verri.
 An Invitation to 3D Vision by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry. (warning TOUGH!!)
Robotics.
 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