Our publications (including those co-authored by our members)
Machine Learning in Finance by Paul Bilokon, Matthew F. Dixon, and Igor Halperin
To be published in 2020! See the book’s page on Springer’s website.
Introduces fundamental concepts in machine learning for canonical modeling and decision frameworks in finance. Presents a unified treatment of machine learning, financial econometrics and discrete time stochastic control problems in finance.
Chapters include examples, exercises and Python codes to reinforce theoretical concepts and demonstrate the application of machine learning to algorithmic trading, investment management and risk management.
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend increasing toward computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.
Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers’ understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher’s perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Machine Learning and Big Data with kdb+/q
Machine Learning and Big Data with kdb+/q is available on Amazon.co.uk.
Upgrade your programming language to more effectively handle high-frequency data.
Machine Learning and Big Data with KDB+/Q offers quants, programmers and algorithmic traders a practical entry into the powerful but non-intuitive kdb+ database and q programming language.
Ideally designed to handle the speed and volume of high-frequency financial data at sell- and buy-side institutions, these tools have become the de facto standard; this book provides the foundational knowledge practitioners need to work effectively with this rapidly-evolving approach to analytical trading.
The discussion follows the natural progression of working strategy development to allow hands-on learning in a familiar sphere, illustrating the contrast of efficiency and capability between the q language and other programming approaches. Rather than an all-encompassing “bible”-type reference, this book is designed with a focus on real-world practicality to help you quickly get up to speed and become productive with the language.
Understand why kdb+/q is the ideal solution for high-frequency data. Delve into the “meat” of q programming to solve practical economic problems. Perform everyday operations including basic regressions, cointegration, volatility estimation, modelling and more. Learn advanced techniques from market impact and microstructure analyses to machine learning techniques including neural networks.
The kdb+ database and its underlying programming language q offer unprecedented speed and capability. As trading algorithms and financial models grow ever more complex against the markets they seek to predict, they encompass an ever-larger swath of data – more variables, more metrics, more responsiveness and altogether more “moving parts.”
Traditional programming languages are increasingly failing to accommodate the growing speed and volume of data, and lack the necessary flexibility that cutting-edge financial modelling demands. Machine Learning and Big Data with KDB+/Q opens up the technology and flattens the learning curve to help you quickly adopt a more effective set of tools.
Trading Thalesians by Saeed Amen
Trading Thalesians is available on Amazon.co.uk.
What can the ancient world teach us about modern money markets? How can we use examples from the ancient world, philosophers and writers to better understand the markets? Just as historians such as Herodotus living in ancient Greece examined the past, can traders look to their past to learn something new?
In this exciting new book, Saeed Amen looks to the ancient world to help us better understand modern money markets, demonstrating what ancient philosophers can teach us about trading markets today, and showing readers how to maximise their returns.
Based on the rationale that if your primary objective is purely to make money from trading quickly, you can make decisions that perversely increase the likelihood of losing; this book demonstrates how successful trading can actually be achieved as a byproduct of good trading.
Relating concepts from the ancient world, such as water and risk, diversified knowledge, Herodotus and historical bias to the modern world money markets, Amen demonstrates that by focusing on goals that go beyond making money, lateral thinking, targeting risk adjusted returns, and keeping drawdowns in check, investors will indirectly make more money in the long run.
Investors might be fooled by randomness on occasion, but luck can never be derided as an important factor, which helps investors succeed. Instead repeated success in investing capital over an extended period seems to be less a product of randomness, but instead a product of a profound understanding of the markets.
Books on Python programming
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Python (Second Edition) by Wes McKinney
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Python (Second Edition) is available on Amazon.co.uk.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples.
Python for Finance: Mastering Data Driven Finance (Second Edition) by Yves Hilpisch
Python for Finance: Mastering Data Driven Finance (Second Edition) is available on Amazon.co.uk.
The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
Books on kdb+/q
q For Mortals Version 3: An Introduction to q Programming by Jeffry A. Borror
q For Mortals Version 3: An Introduction to q Programming is available on Amazon.co.uk.
Q for Mortals Version 3 is a thorough presentation of the q programming language and an introduction to the kdb+ database. It is a complete rewrite of the original Q for Mortals that is current with q3.3. The presentation is derived from classes taught by the author at international financial institutions over the last decade. It is a series of tutorials based on q snippets intended to be entered interactively into the q console by the reader. The text takes its subject seriously but not itself. Technical explanations are augmented by mathematical observations, references to general programming concepts and other programming languages, and bad jokes. Coding style recommendations and advice to avoid gotchas appear liberally throughout. Examples are as simple as they can be but no simpler. Chapter 1, Q Shock and Awe, provides a piquant panorama of the power of q and its dazzling zen-like nature. Chapter 2 describes the base data types of q. Chapter 3 discusses lists, the fundamental data structure of q Chapter 4 presents the basic operators. Chapter 5 introduces dictionaries, which associate keys and values. Chapter 6 presents an in-depth description of functions and q’s constructs for functional programming. Chapter 7 demonstrates transforming data from one type to another. Chapter 8 introduces tables and keyed tables, the fundamental data structures for kdb. Chapter 9 describes q-sql and all the methods to manipulate tables. Chapter 10 presents ways to control execution of q programs. Chapter 11 covers file and interprocess communication I/O Chapter 12 describes workspace organization and management. Chapter 13 discusses system commands and command line parameters. Chapter 14 serves as an introduction to the kdb+ database. Chapter A has a complete rundown of the built-in functions. Chapter B lists common error messages. A cross-referenced index closes the book.
Q Tips: Fast, Scalable and Maintainable Kdb+ by Nick Psaris
Q Tips: Fast, Scalable and Maintainable Kdb+ is available on Amazon.co.uk.
Learn q by building a real life application. Q Tips teaches you everything you need to know to build a fully functional CEP engine. Advanced topics include profiling an active kdb+ server, derivatives pricing and histogram charting. As each new topic is introduced, tips are highlighted to help you write better q.
Books on linear algebra
Linear Algebra and Learning from Data by Gilbert Strang
Linear Algebra and Learning from Data is available on Amazon.co.uk.
Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Linear Algebra (third edition) by John B. Fraleigh and Raymond A. Beauregard
Linear Algebra is available on Amazon.co.uk.
Fraleigh and Beauregard offer a good undergraduate-level textbook on linear algebra, starting from the basics and introducing some nontrivial applications.
Schaum’s Outline of Linear Algebra (Schaum’s Outlines, sixth edition) by Seymour Lipschutz and Marc Lipson
Schaum’s Outline of Linear Algebra is available on Amazon.co.uk.
Fortunately, there’s Schaum’s. This all-in-one-package includes more than 600 fully-solved problems, examples, and practice exercises to sharpen your problem-solving skills. Plus, you will have access to 25 detailed videos featuring math instructors who explain how to solve the most commonly tested problems–it’s just like having your own virtual tutor! You’ll find everything you need to build confidence, skills, and knowledge for the highest score possible.
More than 40 million students have trusted Schaum’s to help them succeed in the classroom and on exams. Schaum’s is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. Helpful tables and illustrations increase your understanding of the subject at hand.
Schaum’s Outline of Linear Algebra, Sixth Edition features:
- Updated content to match the latest curriculum
- Over 600 problems with step-by-step solutions
- An accessible outline format for quick and easy review
- Clear explanations for all linear algebra concepts
- Access to revised Schaums.com website and new app with access to 25 problem-solving videos, and more
Books on probability theory
Schaum’s Outlines: Probability and Statistics (fourth edition) by Murray R. Spiegel, John Schiller, and R. Alu Srinivasan
Schaum’s Outline of Probability and Statistics is available on Amazon.co.uk.
Tough Test Questions? Missed Lectures? Not Enough Time?
Fortunately, there’s Schaum’s. This all-in-one-package includes more than 750 fully solved problems, examples, and practice exercises to sharpen your problem-solving skills. Plus, you will have access to 20 detailed videos featuring Math instructors who explain how to solve the most commonly tested problems–it’s just like having your own virtual tutor! You’ll find everything you need to build confidence, skills, and knowledge for the highest score possible.
More than 40 million students have trusted Schaum’sto help them succeed in the classroom and on exams.Schaum’s is the key to faster learning and highergrades in every subject. Each Outline presents all theessential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills.
This Schaum’s Outline gives you
- 897 fully solved problems
- Concise explanations of all course fundamentals
- Information on conditional probability andindependence, random variables, binominal and normal distributions, sampling distributions, and analysis of variance
Fully compatible with your classroom text, Schaum’s highlights all the important facts you need to know. Use Schaum’s to shorten your study time–and get your best test scores!
Schaum’s Outlines–Problem Solved.
Books on optimisation
Convex Optimization by Stephen Boyd and Lieven Vandenberghe
Convex Optimization is available on Amazon.co.uk.
Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Practical Optimization by Philip E. Gill, Walter Murray, and Margaret H. Wright
Practical Optimization is available on Amazon.co.uk.
This book is designed to help problem solvers make the best use of optimization software–i.e., to use existing methods most effectively whenever possible, and to adapt and modify techniques for particular problems if necessary. The contents of this book therefore include some topics that are essential for all those who wish to solve optimization problems. In addition, certain topics are treated that should be of special interest in most practical optimization problems. For example, advice is given to users who wish to take an active role in formulating their problems so as to enhance the chances of solving them successfully, and to users who need to understand why a certain method fails to solve their problem.
Optimization for Machine Learning by Suvrit Sra, Francis Bach, and Stephen J. Wright
Optimization for Machine Learning is available on Amazon.co.uk.
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today’s machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Optimization Methods in Finance by Gerard Cornuejols and Reha Tutuncu
Optimization Methods in Finance is available on Amazon.co.uk.
Optimization Methods in Finance Graduate textbook which applies recent advances in optimization methods to solve practical problems in mathematical finance.
Books on Statistics
Statistics and Finance: An Introduction (Springer Texts in Statistics) by David Ruppert
Statistics and Finance: An Introduction is available on Amazon.co.uk.
This book emphasizes the applications of statistics and probability to finance. The basics of these subjects are reviewed and more advanced topics in statistics, such as regression, ARMA and GARCH models, the bootstrap, and nonparametric regression using splines, are introduced as needed. The book covers the classical methods of finance and it introduces the newer area of behavioral finance. Applications and use of MATLAB and SAS software are stressed. The book will serve as a text in courses aimed at advanced undergraduates and masters students. Those in the finance industry can use it for self-study.
Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics, second edition) by David Ruppert and David S. Matteson
Statistics and Data Analysis for Financial Engineering: with R examples is available on Amazon.co.uk.
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
Intelligent Data Analysis: An Introduction (second edition) by Michael R. Berthold and David Hand
Intelligent Data Analysis: An Introduction is available on Amazon.co.uk.
This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.
An Introduction to Statistical Learning with Applications in R (Springer Texts in Statistics) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
An Introduction to Statistical Learning with Applications in R is available on Amazon.co.uk.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics, Second Edition) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
The Elements of Statistical Learning: Data Mining, Inference, and Prediction is available on Amazon.co.uk.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.
Books on time series analysis
Practical Time Series Analysis: Prediction with Statistics & Machine Learning by Aileen Nielsen
Practical Time Series Analysis: Prediction with Statistics & Machine Learning is available on Amazon.co.uk.
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance.
Time Series Forecasting: An Applied Machine Learning Approach by Francesca Lazzeri
Time Series Forecasting: An Applied Machine Learning Approach is available on Amazon.co.uk.
Learn how to build and operationalize machine learning forecast models for your everyday projects. With this practical book, experienced and novice data scientists, business analysts, and AI developers will learn the steps necessary for building, training, and deploying time series forecasting models for their organizations. Time series data is an invaluable source of information used for future strategy and planning operations in several industries. From finance to education and health care, time series forecasting plays a major role in unlocking business insights with respect to time. During the past few decades, machine learning model-based forecasting has become popular in both the private and the public decision-making process.
Books on Machine Learning
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd edition) by Aurelien Geron
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems is available on Amazon.co.uk.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets.
Deep Learning with Python by Francois Chollet
Deep Learning with Python is available on Amazon.co.uk.
Deep learning is applicable to a widening range of artificial
intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition.
Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects.
Deep Learning (Adaptive Computation and Machine Learning Series) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep Learning is available on Amazon.co.uk.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” -Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series, second edition) by Richard S. Sutton and Andrew G. Barto
Reinforcement Learning: An Introduction is available on Amazon.co.uk.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field’s key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning’s relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson’s wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Deep Reinforcement Learning in Action by Alex Zai and Brandon Brown
Deep Reinforcement Learning in Action is available on Amazon.co.uk.
Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot.
Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.
Dynamic Programming and Optimal Control, 4th Edition, by Dimitri P. Bertsekas
Dynamic Programming and Optimal Control, Vol. I is available on Amazon.co.uk.
Dynamic Programming and Optimal Control, Vol. II is available on Amazon.co.uk.
This two-volume book on dynamic programming and optimal control covers discounted problems (theory and computational methods), stochastic shortest path problems, undiscounted problems, average cost per stage problems, approximate dynamic programming, and contains an appendix on measure-theoretic issues in dynamic programming.
The fourth edition (February 2017) contains a substantial amount of new material, particularly on approximate DP in Chapter 6. This chapter was thoroughly reorganised and rewritten, to bring it in line, both with the contents of Vol. II, whose latest edition appeared in 2012, and with recent developments, which have propelled approximate DP to the forefront of attention
Some of the highlights of the revision of Chapter 6 are in increased emphasis on one-step and multistep lookahead methods, parametric approximation architectures, neural networks, rollout, and Monte Carlo tree search. Among other applications, these methods have been instrumental in recent spectacular success of computer Go programs. The material on approximate DP also provides an introduction and some perspective for the more analytically oriented treatment of Vol. II.
The fourth edition of Vol. II of the two-volume DP book is a major revision and contains a substantial amount of new material, as well as a reorganisation of old material. The length has increased by more than 60% from the third edition, and most of the old material has been restructured and/or revised. Volume II now numbers more than 700 pages and is larger in size than Vol. I. It can arguably be viewed as a new book!
Approximate DP has become the central focal point of this volume, and occupies more than half of the book (the last two chapters, and large parts of Chapters 1-3). Thus one may also view this new edition as a followup of the author’s 1996 book “Neuro-Dynamic Programming” (coauthored with John Tsitsiklis). A lot of new material, the outgrowth of research conducted in the six years since the previous edition, has been included.
A new printing of the fourth edition (January 2018) contains some updated material, particularly on undiscounted problems in Chapter 4, and approximate DP in Chapter 6. References were also made to the contents of the 2017 edition of Vol. I, and to high profile developments in deep reinforcement learning, which have brought approximate DP to the forefront of attention.
Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play by David Foster
Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play is available on Amazon.co.uk.
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.
Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.
- Discover how variational autoencoders can change facial expressions in photos
- Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
- Create recurrent generative models for text generation and learn how to improve the models using attention
- Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
- Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Books on applications of Machine Learning to finance
Advances in Financial Machine Learning by Marcos Lopez de Prado
Advances in Financial Machine Learning is avialable on Amazon.co.uk.
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Books on applications of Machine Learning to life sciences
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More by Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More is available on Amazon.co.uk.
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine-an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working.
Books on quantum computing
Quantum Computing: A Gentle Introduction by Eleanor G. Rieffel and Wolfgang Polak
Quantum Computing: A Gentle Introduction is available on Amazon.co.uk.
A thorough exposition of quantum computing and the underlying concepts of quantum physics, with explanations of the relevant mathematics and numerous examples. The combination of two of the twentieth century’s most influential and revolutionary scientific theories, information theory and quantum mechanics, gave rise to a radically new view of computing and information. Quantum information processing explores the implications of using quantum mechanics instead of classical mechanics to model information and its processing. Quantum computing is not about changing the physical substrate on which computation is done from classical to quantum but about changing the notion of computation itself, at the most basic level. The fundamental unit of computation is no longer the bit but the quantum bit or qubit. This comprehensive introduction to the field offers a thorough exposition of quantum computing and the underlying concepts of quantum physics, explaining all the relevant mathematics and offering numerous examples. With its careful development of concepts and thorough explanations, the book makes quantum computing accessible to students and professionals in mathematics, computer science, and engineering. A reader with no prior knowledge of quantum physics (but with sufficient knowledge of linear algebra) will be able to gain a fluent understanding by working through the book.
Programming Quantum Computers: Essential Algorithms and Code Samples by Eric R. Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia
Programming Quantum Computers: Essential Algorithms and Code Samples is available on Amazon.co.uk.
Quantum computers are poised to kick-start a new computing revolution—and you can join in right away. If you’re in software engineering, computer graphics, data science, or just an intrigued computerphile, this book provides a hands-on programmer’s guide to understanding quantum computing. Rather than labor through math and theory, you’ll work directly with examples that demonstrate this technology’s unique capabilities.
Quantum computing specialists Eric Johnston, Nic Harrigan, and Mercedes Gimeno-Segovia show you how to build the skills, tools, and intuition required to write quantum programs at the center of applications. You’ll understand what quantum computers can do and learn how to identify the types of problems they can solve.
This book includes three multichapter sections:
- Programming for a QPU—Explore core concepts for programming quantum processing units, including how to describe and manipulate qubits and how to perform quantum teleportation.
- QPU Primitives—Learn algorithmic primitives and techniques, including amplitude amplification, the Quantum Fourier Transform, and phase estimation.
- QPU Applications—Investigate how QPU primitives are used to build existing applications, including quantum search techniques and Shor’s factoring algorithm.
MIT course Linear Alebra (18.06) by Prof. Gilbert Strang
The course materials including the videos are available online.
This course parallels the combination of theory and applications in Prof. Strang’s textbook Introduction to Linear Algebra. The course picks out four key applications in the book: Graphs and Networks, Systems of Differential Equations, Least Squares and Projections, and Fourier Series and the Fast Fourier Transform.
This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices.
MIT course Multivariable Calculus (18.02) by Prof. Denis Auroux
The course materials including the videos are available online.
This course covers vector and multi-variable calculus. It is the second semester in the freshman calculus sequence. Topics include vectors and matrices, partial derivatives, double and triple integrals, and vector calculus in 2 and 3-space.
MIT OpenCourseWare offers another version of 18.02, from the Spring 2006 term. Both versions cover the same material, although they are taught by different faculty and rely on different textbooks. Multivariable Calculus (18.02) is taught during the Fall and Spring terms at MIT, and is a required subject for all MIT undergraduates.
Stanford course Convex Optimization I (EE364a) by Prof. Stephen Boyd
The course materials including the videos are available online.
Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.