10 Free Must-read Books on AI
Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.
The media, industry, and academia can’t seem to get enough of AI. The hype is everywhere, but the applications are real. Companies are scrambling to leverage AI into their business systems, start-ups are bringing new tools and applications to the masses, and research institutions are expanding their AI programs with money flowing in from public and private funding sources.
The term Artificial Intelligence was first coined by Stanford computer scientist John McCarthy with his proposal of the Dartmouth Conference, the first academic conference covering the topic hosted during the summer of 1956, which launched the field. During the following decades, much has been developed in AI, although an explosion in research and knowledge has occurred more recently as computational power ramps up to meet the demands of machine learning algorithms and Big Data.
There is much to learn in AI to get started in the field, and a dedication to continual learning is necessary to remain current. We curated a set of 10 books available online – totaling 4,738 pages -- to bring you a depth and breadth of familiarity with AI from its statistical foundation and algorithmic applications to ethical considerations and how to leverage AI as a business leader. Take advantage of these free online resources to help you launch your academic studies or next career, or to help bring you up to speed as a current practitioner.
Each book may either be accessed online through a web site or downloaded as a PDF document. Book descriptions are based directly on the text provided by the author or publisher. This list builds on our previous “Must-read” machine learning books featuring by KDnuggets from 2017, 2018, and earlier in 2019. So, dive into this swath of free AI knowledge, and be a part of the scientific and engineering adventure that is shaping our world today and will define our future.
1(a). Artificial Intelligence: Foundations of Computational Agents, 2nd Edition
by David L. Poole and Alan K. Mackworth (September 2017, 820 pages).
About the book: Presenting AI using a coherent framework to study the design of intelligent computational agents, this book shows how the basic approaches fit into a multidimensional design space, so you can learn the fundamentals without losing sight of the bigger picture. This new edition also features expanded coverage on machine learning material as well as the social and ethical consequences of AI and ML. With a balance of theory and experiment linked together, the science of AI is developed with its engineering applications. Structured as textbook, the book remains straightforward and self-contained to support an audience of professionals, researchers, and independent learners.
About the authors: David L. Poole is a Professor of Computer Science at the University of British Columbia and co-author of three artificial intelligence books. He is a former Chair of the Association for Uncertainty in Artificial Intelligence, the winner of the Canadian AI Association (CAIAC) 2013 Lifetime Achievement Award, and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and CAIAC.
Alan K. Mackworth is a Professor of Computer Science at the University of British Columbia who has authored over 130 papers and co-authored two books. His awards include the Artificial Intelligence Journal (AIJ) Classic Paper Award and the Association of Constraint Programming (ACP) Award for Research Excellence, and he served as President of the International Joint Conference on Artificial Intelligence (IJCAI), the Association for the Advancement of Artificial Intelligence (AAAI), and the Canadian AI Association (CAIAC). He is a Fellow of AAAI, CAIAC and the Royal Society of Canada.
1(b). Python code for Artificial Intelligence: Foundations of Computational Agents
by David L. Poole and Alan K. Mackworth (September 2019, 223 pages).
This is a supporting book for Pool and Mackworth’s book above that contains pages of reusable Python code along with additional discussions and descriptions.
2. Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning
by Andrew Ng (2018, 118 pages).
About the book: While KDnuggets previously featured this influential book in our April 2017 list of recommended reads, it is worth highlighting again for an AI focus because it is continually updated. The current draft is from 2018, so be sure to check out its latest updates.
AI is transforming numerous industries, and this book teaches you how to structure machine learning projects. Not focused on teaching ML algorithms, the book instead looks at how to make ML algorithms work. After reading this book, you will be able to prioritize the most promising directions for an AI project, diagnose errors in a machine learning system, build ML in complex settings, such as mismatched training and test sets, set up an ML project to compare to or surpass human-level performance, and know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
About the author: Andrew Ng co-founded and led Google Brain and was a VP and Chief Scientist of Baidu. In 2011, he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and taught an online Machine Learning class offered to over 100,000 students, leading to Coursera for which he is the Co-Chairman and Co-Founder as well as an Adjunct Professor at Stanford University.
3. Deep Learning
by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (November 2016, 800 pages).
About the book: Also featured in our April 2017 listing of books for machine learning, this is another key resource for learning about AI because of its focus on deep learning. It is recommended for all students who are working on mastering deep learning with comprehensive mathematical and conceptual coverage of Monte Carlo methods, recurrent and recursive nets, autoencoders, and deep generative models.
About the authors: Ian Goodfellow earned his B.S. and M.S. in computer science from Stanford University under Andrew Ng followed by a Ph.D. in machine learning from the Université de Montréal with Yoshua Bengio and Aaron Courville. He next joined Google as part of the Google Brain research team after which he joined the newly founded OpenAI institute before returning to Google Research in 2017. Known for inventing generative adversarial networks, Ian is also a lead author of the textbook Deep Learning, was cited in MIT Technology Review's 35 Innovators Under 35, and was included in Foreign Policy's list of 100 Global Thinkers.
Yoshua Bengio is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA), most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning.
Aaron Courville is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and a member of MILA.
4. The Quest for Artificial Intelligence: A History of Ideas and Achievements
by Nils J. Nilsson (October 2009, 707 pages).
About the book: This book traces the history of AI from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers. With AI becoming so much a part of everyone’s life, the technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots. The book’s many diagrams and easy-to-understand descriptions of AI programs you gain an understanding of how these and other AI systems work. End-of-chapter notes containing citations to valuable source materials are of great use to AI scholars and researchers, and the book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.
About the author: Nils J. Nilsson, Kumagai Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford University, California, received his Ph.D. degree in Electrical Engineering from Stanford in 1958. He spent twenty-three years at the Artificial Intelligence Center of SRI International working on statistical and neural-network approaches to pattern recognition, co-inventing the A* heuristic search algorithm and the STRIPS automatic planning system, directing work on the integrated mobile robot, SHAKEY, and collaborating in the development of the PROSPECTOR expert system. He published five textbooks on artificial intelligence, taught courses on artificial intelligence and machine learning, and researched flexible robots that react to dynamic worlds, plan courses of action, and learn from experience. Professor Nilsson served on the editorial boards of the journal Artificial Intelligence and of the Journal of Artificial Intelligence Research and was an Area Editor for the Journal of the Association for Computing Machinery. He is a past-president and Fellow of the American Association for Artificial Intelligence, a Fellow of the American Association for the Advancement of Science, and recipient of the IEEE Neural-Network Pioneer award, the IJCAI Research Excellence award, and the AAAI Distinguished Service award.
5. Reinforcement Learning: An Introduction, 2nd edition
by Richard S. Sutton and Andrew G. Barto (November 2018, 548 pages).
About the book: A widely used text on reinforcement learning, which is one of the most active research areas in artificial intelligence, this book provides a clear and simple account of the field's key ideas and algorithms. With a focus on core online learning algorithms, including UCB, Expected Sarsa, and Double Learning, it then extends these ideas to function approximation covering topics on artificial neural networks and the Fourier basis. This second edition includes new chapters on reinforcement learning's relationships to psychology and neuroscience as well as updated case-studies on AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy.
About the authors: Richard S. Sutton is a distinguished research scientist at DeepMind in Edmonton and a professor in the Department of Computing Science at the University of Alberta. He previously worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a Ph.D. in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. Prof. Sutton is also a fellow of the Royal Society of Canada, the Association for the Advancement of Artificial Intelligence, the Alberta Machine Intelligence Institute, and CIFAR.
Andrew Barto is Professor Emeritus of Computer Science, University of Massachusetts, Amherst, where he served as Chair of the UMass Department of Computer Science from 2007 to 2011. He received his B.S. with distinction in mathematics from the University of Michigan in 1970, and his Ph.D. in Computer Science in 1975, also from the University of Michigan. Before retiring, he co-directed the Autonomous Learning Laboratory at UMass Amherst, and is currently an Associate Member of the Neuroscience and Behavior Program of the University of Massachusetts, an associate editor of Neural Computation, a member of the Advisory Board of the Journal of Machine Learning Research, and a member of the editorial board of Adaptive Behavior. Prof. Barto is a Fellow of the American Association for the Advancement of Science, a Fellow and Senior Member of the IEEE, and a member of the Society for Neuroscience. He received the 2004 IEEE Neural Network Society Pioneer Award, and the IJCAI-17 Award for Research Excellence.
6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition (corrected)
by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (January 2017, 764 pages).
About the book: Yet another previous KDnuggets feature in 2017, this is a newer edition of the book that remains a core foundational for AI research and study. The 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, and 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, with its many topics including neural networks, support vector machines, classification trees, boosting, graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
About the authors: Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area, with Hastie and Tibshirani having developed generalized additive models. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.
7. A Brief Introduction to Neural Networks
by David Kriesel, manuscript (2007, 244 pages)
About the book: Neural networks are bio-inspired mechanisms for data processing that enable computers to learn in a way that is technically similar to a biological brain. These approaches can even generalize once solutions to enough problem instances are taught to the algorithms. This manuscript is self-published by the author and provides a comprehensive resource on the topic that is crucial foundational knowledge for AI research and investigation.
About the author: David Kriesel is a Technology Leader for Data Science and Machine Learning at Procter & Gamble where he works on strategic and hands-on topics. Previously, David was Head of Release Engineering at IVU Traffic Technologies AG and was responsible for enabling techniques and working paradigms in the DevOps field across six development teams in two departments.
8. Advanced Machine Learning with Python
by John Hearty (July 2016, 278 pages)
Download (free registration with Packt required).
About this book: A guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book will help you push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques and semi-supervised learning while working with real-world applications that include image, music, text, and financial data. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering. Specifically, you will learn how to resolve complex machine learning problems and explore deep learning, learn to use Python code for implementing a range of machine learning algorithms and techniques, and tackle real-world computing problems through rigorous and effective approaches.
About the author: John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started in mobile gaming, he later joined Microsoft to develop player modeling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Eventually, John became a consultant offering comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities.
9. Ethical Artificial Intelligence
by Bill Hibbard (November 2015, 177 pages).
About the book: This book-length article combines several peer-reviewed papers and material to analyze the issues of ethical artificial intelligence. The behavior of future AI systems can be described by mathematical equations, which are adapted to analyze possible unintended AI behaviors and ways that AI designs can avoid them. This article makes a case for utility-maximizing agents and for avoiding infinite sets in agent definitions as well as how to avoid agent self-delusion using model-based utility functions and how to avoid agents that corrupt their reward generators (sometimes called "perverse instantiation") using utility functions that evaluate outcomes from the perspective of humans. The article also argues that agents can avoid unintended instrumental actions (sometimes called "basic AI drives" or "instrumental goals") by accurately learning human values. A self-modeling agent framework is defined to show how it can avoid problems of resource limits, being predicted by other agents, and inconsistency between the agent's utility function and its definition. Finally, the article discusses how future AI will differ from current AI, the politics of AI, and the ultimate use of AI to help understand the nature of the universe and our place in it.
About the author: Bill Hibbard is an Emeritus Senior Scientist at the University of Wisconsin-Madison Space Science and Engineering Center, currently working on issues of AI safety and unintended behaviors. He has a BA in Mathematics and MS and Ph.D. in Computer Sciences, all from the University of Wisconsin-Madison. He is also the author of Super-Intelligent Machines, “Avoiding Unintended AI Behaviors,” and “Decision Support for Safe AI Design,” and principal author of the Vis5D, Cave5D, and VisAD open-source visualization systems.
10. The Essential AI Handbook for Leaders
by Peltarion (59 pages)
About the book: While this book was developed as a marketing feature from the company, Peltarion, it provides an important overview that business leaders should appreciate when leveraging AI. With the potential to transform countless aspects of business and society for the better, this book is intended to help more people understand what AI is and how businesses and organizations can harness the technology. Featuring a foreword by Marcus Wallenberg, Chairman of SEB, SAAB, and FAM, and an introduction by Peltarion founder and CEO Luka Crnkovic-Friis, the book explains the fundamentals of AI, its potential benefits, and how businesses can make AI operational to create positive change.
- 10 Free Must-Read Books for Machine Learning and Data Science (2017)
- 10 More Free Must-Read Books for Machine Learning and Data Science (2018)
- Another 10 Free Must-Read Books for Machine Learning and Data Science (2019)
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