{"id":26819,"date":"2021-08-04T06:00:00","date_gmt":"2021-08-04T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26819"},"modified":"2021-08-05T08:35:13","modified_gmt":"2021-08-05T15:35:13","slug":"book-review-mathematics-for-machine-learning","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/08\/04\/book-review-mathematics-for-machine-learning\/","title":{"rendered":"Book Review: Mathematics for Machine Learning"},"content":{"rendered":"\n
\"\"<\/figure><\/div>\n\n\n\n

This article represents the next installment of my reviews of books with a focus on the mathematics of machine learning<\/strong>. I’m energized about all the new learning resources coming out with alignment around this topic. As I mention to my Introduction to Data Science<\/em> students, it is important for all data scientists to have a command of the theoretical foundations for our field. Without this, we’re really just guessing when it comes to performing tasks like hyperparameter tuning. “Mathematics for Machine Learning<\/a>” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I’ve reviewed thus far, this is my favorite. Read on to learn why. <\/p>\n\n\n\n

Excellent Coverage<\/strong><\/p>\n\n\n\n

As exhibited in the Table of Contents below, this book has excellent coverage for all important topic areas. I found Part I, Mathematical Foundations, a one-stop-shop for all the mathematical background necessary to appreciate all the ML-specific topics in Part II. There’s really no need for multiple textbooks on linear algebra and vector calculus for example. You can quickly get up to speed with these topics by methodically reading the chapters. I also appreciate the logical progression of topics as it makes total sense for getting a solid foundation for the mathematics of ML. <\/p>\n\n\n\n

Part I: Mathematical Foundations<\/p>\n\n\n\n

  1. Introduction and Motivation<\/li>
  2. Linear Algebra<\/li>
  3. Analytic Geometry<\/li>
  4. Matrix Decompositions<\/li>
  5. Vector Calculus<\/li>
  6. Probability and Distribution<\/li>
  7. Continuous Optimization<\/li><\/ol>\n\n\n\n

    Part II: Central Machine Learning Problems<\/p>\n\n\n\n

    1. When Models Meet Data<\/li>
    2. Linear Regression<\/li>
    3. Dimensionality Reduction with Principal Component Analysis<\/li>
    4. Density Estimation with Gaussian Mixture Models<\/li>
    5. Classification with Support Vector Machines<\/li><\/ol>\n\n\n\n

      Beautifully Produced<\/strong><\/p>\n\n\n\n

      When I receive a review copy of a new book from the publisher, I’m never sure of the level of publication quality I might encounter. Some books are flimsy, some are poorly edited, and others do silly things like publish color data visualizations in black & white. This book, on the other hand, is spectacular! The production quality is very high, and the figures, oh the figures! I’ve never seen a math book come alive like this one does, and colorful and well-thought-out graphics feed the senses, and carefully aid in communication of such a deep and technical subject. For instance, every chapter includes a “Mind Map” that is an outline of all the topics covered and how they’ll be used in subsequent chapters. Why can’t all books include this useful guide to learning? <\/p>\n\n\n\n

      \"\"
      Mind Map of the concepts introduced in Chapter 5 on Vector Calculus<\/figcaption><\/figure><\/div>\n\n\n\n

      Mathematical Clarity<\/strong><\/p>\n\n\n\n

      \"\"
      Visualization of gradient computation of a matrix with respect to a vector<\/figcaption><\/figure><\/div>\n\n\n\n

      The book includes very clear and concise mathematics with no “hand waving” in the derivations but instead every chapter has many long worked-out “Examples” that drill-down into the theory. Again, the authors include beautiful visualizations designed to aid in the understanding of the math as depicted in the adjacent figure. Further, each chapter includes well-crafted exercises to help the reader hone their understanding of the topics. Some of my favorite treatments in the book include: singular value decomposition (Section 4.5), Gradients of Vector-valued Functions (Section 5.3), Optimization Using Gradient Descent (Section 7.1), Bayesian Linear Regression (Section 9.3), and Dimensionality Reduction with PCA (Chapter 10). <\/p>\n\n\n\n

      Book Available for Free<\/strong><\/p>\n\n\n\n

      The final big reason to like this book is it is available as a free download<\/a> on the website<\/a> the authors set-up. The site also includes a number of Jupyter notebook tutorials for learning and solutions, along with a series of compelling videos and slides from NeuralIPS 2020 on integration and differentiation. <\/p>\n\n\n\n

      In conclusion, I would highly recommend this book to any data scientist with a desire to obtain a firm foundation for how machine learning algorithms work. The data scientist who successfully consumes the important material in this book will surface with a new appreciation for what’s going on inside the “black box” of the most popular algorithms. <\/p>\n\n\n\n

      \"\"<\/figure><\/div>\n\n\n\n

      Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies. <\/em><\/p>\n\n\n\n

      Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

      Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

      “Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I’ve reviewed thus far, this is my favorite. Read on to learn why. <\/p>\n","protected":false},"author":37,"featured_media":26820,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[115,92,90,87,180,67,56,1],"tags":[1041,277,705,95],"acf":[],"yoast_head":"\nBook Review: Mathematics for Machine Learning - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2021\/08\/04\/book-review-mathematics-for-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Book Review: Mathematics for Machine Learning - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\""Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I've reviewed thus far, this is my favorite. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/07\/Mathematics-ML-book.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-6Yz","jetpack-related-posts":[{"id":24399,"url":"https:\/\/insidebigdata.com\/2020\/06\/11\/book-review-linear-algebra-and-learning-from-data-by-gilbert-strang\/","url_meta":{"origin":26819,"position":0},"title":"Book Review: Linear Algebra and Learning from Data by Gilbert Strang","date":"June 11, 2020","format":false,"excerpt":"I've been a big fan of MIT mathematics professor Dr. Gilbert Strang for many years. A few years ago I reviewed the latest 5th edition of his venerable text on linear algebra. Then last year I learned how he morphed his delightful mathematics book into a brand new title (2019)\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/05\/Strang_learning_from_data_book.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":21430,"url":"https:\/\/insidebigdata.com\/2018\/11\/08\/math-behind-machine-learning\/","url_meta":{"origin":26819,"position":1},"title":"The Math Behind Machine Learning","date":"November 8, 2018","format":false,"excerpt":"What math subjects are used in machine learning, and how are they used? In this research paper by Richard Han, Ph.D., we look at the mathematics behind the machine learning techniques linear regression, linear discriminant analysis, logistic regression, artificial neural networks, and support vector machines.","rel":"","context":"In "Featured"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/11\/Richard-Han-paper.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":25038,"url":"https:\/\/insidebigdata.com\/2020\/09\/28\/book-review-artificial-intelligence-engines-a-tutorial-introduction-to-the-mathematics-of-deep-learning\/","url_meta":{"origin":26819,"position":2},"title":"Book Review: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning","date":"September 28, 2020","format":false,"excerpt":"We're seeing a rising number of new books on the mathematics of data science, machine learning, AI and deep learning, which I view as a very positive trend because of the importance for data scientists to understand the theoretical foundations for these technologies. In the coming months, I plan to\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/09\/Artificial-Intelligence-Engines-pic1.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":15547,"url":"https:\/\/insidebigdata.com\/2016\/07\/28\/book-review-introduction-to-linear-algebra-by-gilbert-strang\/","url_meta":{"origin":26819,"position":3},"title":"Book Review: Introduction to Linear Algebra by Gilbert Strang","date":"July 28, 2016","format":false,"excerpt":"In this book review, I take a close look at the 5th edition of \"Introduction to Linear Algebra\" (Wellesley-Cambridge Press) by MIT mathematics professor Gilbert Strang. This book is a must-have for any serious data scientist.","rel":"","context":"In "Book Review"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":13938,"url":"https:\/\/insidebigdata.com\/2016\/01\/12\/book-review-master-algorithm\/","url_meta":{"origin":26819,"position":4},"title":"Book Review: The Master Algorithm","date":"January 12, 2016","format":false,"excerpt":"I've been waiting for good book that introduces the concepts of data science and machine learning for a lay audience. Then I read an announcement of a new book that seemed to fill this need. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World\u2026","rel":"","context":"In "Book Review"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":13522,"url":"https:\/\/insidebigdata.com\/2015\/08\/14\/book-review-the-manga-guide-to-linear-algebra\/","url_meta":{"origin":26819,"position":5},"title":"Book Review: The Manga Guide to Linear Algebra","date":"August 14, 2015","format":false,"excerpt":"I was happy to receive a review copy of book employing a very unique approach for teaching mathematics, \"The Manga Guide to Linear Algebra,\" published by No Starch Press. This is a comic book, perfect for new data scientists! The book is great for newbies because it clearly spells out\u2026","rel":"","context":"In "Book Review"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2015\/08\/Manga_Linear_Algebra.jpg?resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/26819"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=26819"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/26819\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/26820"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=26819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=26819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=26819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}