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Must Read: Books for learning Machine Learning and Artificial Intelligence

What are your favorite books on Machine Learning or Artificial Intelligence? In the series of must read books, I feature theme-specific lists of books on various topics. Here is the start of the series covering the theme Machine Learning and Artificial Intelligence. On purpose, it’s a fairly condensed list of six books. So I trust that you have a few suggestions what piece could make a good addition.

Please contribute on GitHub to grow the list. Thank you for your help.

Disclaimer: In this list, all books are publicly available, which is fortunate. With my reading lists, I’m generally trying to include as many open source books as I can. Please bear with me or the authors if that cannot be arranged with every item of my lists.

Artificial Intelligence: Foundations of Computational Agents, 2nd Edition by David L. Poole and Alan K. Mackworth (September 2017, 820 pages). My comment: Great framework on the foundations of AI, or “Computational Agents” as it’s referred to in the book. It’s a good choice to get started and understand the bigger picture of AI.

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). My comment: Okay, look. The book covers supervised learning, unsupervised learning, 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. You see?

Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning by Andrew Ng (2018, 118 pages). My comment: If you’re interested in how to make Machine Learning work, Andrew Ng will teach you. I found it particularly valuable to learn how to set up end-to-end ML projects and make sense of its outcomes.

Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (November 2016, 800 pages). My comment: Indisputably, this is a key resource for learning about deep learning. The book uncovers paths to mastering deep learning with comprehensive mathematical and conceptual coverage on various methods.

Reinforcement Learning: An Introduction, 2nd edition by Richard S. Sutton and Andrew G. Barto (November 2018, 548 pages). My comment: Besides deep learning, reinforcement learning is a big topic in AI. This introductory piece will get you up to speed with the methodology and its use.

The Essential AI Handbook for Leaders by Peltarion (59 pages). My comment: This is the one and only non-technical book in the list. It focuses more on how to lead the change in our social and business communities. Noteworthy, the book provides an AI checklist for comparing yourself against AI readiness.

And that’s it. As you have observed, the reading list was condensed to a set of six books. That may appear a little unfair to the hundreds or thousands of other very well-crafted publications. Due to the effects of selective attention, I wanted to focus on the most relevant.

What’s your must read? I may even have missed it. Please let me know. It’d be exciting to learn what’s your favorite books on Machine Learning or Artificial Intelligence.

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