Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online
Mathematics for sale Machine lowest Learning online__front

Description

Product Description

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book''s web site.

Review

‘This book provides great coverage of all the basic mathematical concepts for machine learning. I''m looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.'' Joelle Pineau, McGill University, Montreal

‘The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.'' Christopher Bishop, Microsoft Research Cambridge

''This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.'' Pieter Abbeel, University of California, Berkeley

Book Description

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

About the Author

Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London. Prior to this, he was a faculty member in the Department of Computing, Imperial College London. His research areas include data-efficient learning, probabilistic modeling, and autonomous decision making. Deisenroth was Program Chair of the European Workshop on Reinforcement Learning (EWRL) 2012 and Workshops Chair of Robotics Science and Systems (RSS) 2013. His research received Best Paper Awards at the International Conference on Robotics and Automation (ICRA) 2014 and the International Conference on Control, Automation and Systems (ICCAS) 2016. In 2018, he was awarded the President''s Award for Outstanding Early Career Researcher at Imperial College London. He is a recipient of a Google Faculty Research Award and a Microsoft P.hD. grant.

A. Aldo Faisal leads the Brain and Behaviour Lab at Imperial College London, where he is faculty at the Departments of Bioengineering and Computing and a Fellow of the Data Science Institute. He is the director of the 20Mio£ UKRI Center for Doctoral Training in AI for Healthcare. Faisal studied Computer Science and Physics at the Universität Bielefeld (Germany). He obtained a Ph.D. in Computational Neuroscience at the University of Cambridge and became Junior Research Fellow in the Computational and Biological Learning Lab. His research is at the interface of neuroscience and machine learning to understand and reverse engineer brains and behavior.

Cheng Soon Ong is Principal Research Scientist at the Machine Learning Research Group, Data61, Commonwealth Scientific and Industrial Research Organisation, Canberra (CSIRO). He is also Adjunct Associate Professor at Australian National University. His research focuses on enabling scientific discovery by extending statistical machine learning methods. Ong received his Ph.D. in Computer Science at Australian National University in 2005. He was a postdoc at Max Planck Institute of Biological Cybernetics and Friedrich Miescher Laboratory. From 2008 to 2011, he was a lecturer in the Department of Computer Science at Eidgenössische Technische Hochschule (ETH) Zürich, and in 2012 and 2013 he worked in the Diagnostic Genomics Team at NICTA in Melbourne.

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4.7 out of 54.7 out of 5
335 global ratings

Top reviews from the United States

Estefano Palacios Topic
5.0 out of 5 starsVerified Purchase
Brilliant and Precise
Reviewed in the United States on April 28, 2020
The book is the missing piece between books like Artificial Intelligence: A Modern Approach and the mathematics you require to take such an undertaking. The authors do assume very little prior knowledge from the reader, but it t is recommended that you''ve had exposure to... See more
The book is the missing piece between books like Artificial Intelligence: A Modern Approach and the mathematics you require to take such an undertaking. The authors do assume very little prior knowledge from the reader, but it t is recommended that you''ve had exposure to some of the mathematical topics prior to reading the book. But don''t let that stop you if you''re a beginner: you''ll have to make a few detours to grasp some terms and such. Having said that, a course on single variable calculus ought to be under your belt. That''s basically the only prerequisite.

The explanations are clear, and the book is designed to bring clarity and lucidity onto the topics, not send the student on an endless pit of proofs and rigor.
40 people found this helpful
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Amazon Customer
5.0 out of 5 starsVerified Purchase
Excellent book.. Best in class.
Reviewed in the United States on May 7, 2020
Starts with the basics using clear examples and explanations. Then, moves quickly into intermediate level with practical and relevant information. Lastly, provides useful guidance towards advanced topics. Highly recommended. Best book I have came across so far... See more
Starts with the basics using clear examples and explanations. Then, moves quickly into intermediate level with practical and relevant information. Lastly, provides useful guidance towards advanced topics.

Highly recommended. Best book I have came across so far for starting my own ML journey.
21 people found this helpful
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SB Jones
4.0 out of 5 starsVerified Purchase
A Recommend.
Reviewed in the United States on August 3, 2020
I recommend the book for its clear delineations of sections within chapters. It makes for a good reference for calculations that may not be on the tip of your tongue. I would lodge one major complaint, hence 4 stars, they should make the answers available for the questions... See more
I recommend the book for its clear delineations of sections within chapters. It makes for a good reference for calculations that may not be on the tip of your tongue. I would lodge one major complaint, hence 4 stars, they should make the answers available for the questions at the end of the chapters. Without them, the quizzes are essentially a waste of space as you have no idea if your calculations are correct.
18 people found this helpful
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Srikanth Hanumanthappa
4.0 out of 5 starsVerified Purchase
Best math compendium first Machine Learning
Reviewed in the United States on May 9, 2020
Best book if you are looking to study math of machine learning! Author has given references where to do further studies. If you are beginner to calculus , linear algebra and probability n statistics this is not the book since book expect you at advanced mathematics level Or... See more
Best book if you are looking to study math of machine learning! Author has given references where to do further studies. If you are beginner to calculus , linear algebra and probability n statistics this is not the book since book expect you at advanced mathematics level Or studied the basics of math concepts in your curriculum
21 people found this helpful
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Ihatepickles
5.0 out of 5 starsVerified Purchase
A beautiful book.
Reviewed in the United States on June 24, 2020
Just an unbelievable book. It may be a bit difficult to follow but complemented by a couple of online resources for when you''re stuck it''s awesome. Definitions are precise. Explanations are succinct. It is not intended to be, but is a masterpiece that brings out the... See more
Just an unbelievable book. It may be a bit difficult to follow but complemented by a couple of online resources for when you''re stuck it''s awesome. Definitions are precise. Explanations are succinct.
It is not intended to be, but is a masterpiece that brings out the beauty of mathematics.
19 people found this helpful
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Thomas Paine
4.0 out of 5 starsVerified Purchase
Understanding The Math
Reviewed in the United States on June 11, 2020
This is a subject where people say, "I understand the underlying math." Factually incorrect. The underlying math IS what to understand. I love the topic and I want to expand my depth and breadth of knowledge--and I''m not disappointed.
14 people found this helpful
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Mavichov(gh.liang)
5.0 out of 5 starsVerified Purchase
Great book for beginners!
Reviewed in the United States on September 13, 2020
Even though I can get a free e-copy, I still like the paperback version because I flip through it occasionally. This book sketches a clear big picture of the knowledge tree for ML and provides necessary build blocks to help you build solid foundations in preparation for... See more
Even though I can get a free e-copy, I still like the paperback version because I flip through it occasionally. This book sketches a clear big picture of the knowledge tree for ML and provides necessary build blocks to help you build solid foundations in preparation for practical ML.

You have to be aware this paperback version doesn''t come with solutions. One of my reason to buy this is for the solutions. It turned out that only instructors can request solutions from the press company.
8 people found this helpful
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S Pattanaik
1.0 out of 5 starsVerified Purchase
Not user friendly
Reviewed in the United States on July 22, 2020
The books starts with a nice beginning,but eventually looses the charm and concepts as it progresses. I would suggest ESL a better book than this.
12 people found this helpful
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Top reviews from other countries

Stefan Roesch
3.0 out of 5 starsVerified Purchase
Low proportion of exercises given the material
Reviewed in the United Kingdom on February 20, 2021
While the content of the book is very clear and concise (something many maths books tend to struggle with), this text unfortunately falls into the trap of presenting many new ideas with too few exercises to reinforce them (at least in the maths section). This would be fine...See more
While the content of the book is very clear and concise (something many maths books tend to struggle with), this text unfortunately falls into the trap of presenting many new ideas with too few exercises to reinforce them (at least in the maths section). This would be fine if the authors'' aim was to produce a reference for these topics, but they themselves acknowledge the intended target audience to be "undergraduate students, evening learners and learners participating in online machine learning courses" i.e. those who are being introduced to the subject matter for the first time and who will quickly forget these ideas after having struggled through the terse, potentially unfamiliar, language.
5 people found this helpful
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Amazon Customer
2.0 out of 5 starsVerified Purchase
Surprised at positive reviews
Reviewed in the United Kingdom on August 22, 2021
I find it strange there are so many positive reviews for this book. Some of the reviewers have only this review to their name, which I always find dodgy. The presentation is turgid, dull and unclear. Not a pedagogical masterpiece by any means - neither fish nor fowl, the...See more
I find it strange there are so many positive reviews for this book. Some of the reviewers have only this review to their name, which I always find dodgy. The presentation is turgid, dull and unclear. Not a pedagogical masterpiece by any means - neither fish nor fowl, the terse (as described by the authors themselves) mathematical syntax serves only to obfuscate, without being mathematical rigorous. I''ve made several attempts to read this (and I know much of the material already) but trying to decipher the unnecessarily quasi-mathematical notation that makes simple concepts unclear got too wearying. I''ve ordered Aggarwal''s "Linear Algebra and Optimization for Machine Learning". I hope that''s better.
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Bruno R C Magalhaes
5.0 out of 5 starsVerified Purchase
Very good book to learn the mathematics behind machine learning
Reviewed in the United Kingdom on April 4, 2020
It''s a very (and maybe only) resource for someone moving into machine learning and trying to understand the complexity of the underlying mathematics.
7 people found this helpful
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Happy-go-lucky
5.0 out of 5 starsVerified Purchase
Book is unputdownable. It has surprised my expectation and it is truly well worth my money.
Reviewed in the United Kingdom on April 1, 2021
I have just received this book hours ago. It is paperback. I breezed through book with skimreading at first. Simple illustrations with few colours — VERY HELPFUL. The page layout is perfect, very easy on my eyes! I could read quickly, as the text is not too cluttered. I...See more
I have just received this book hours ago. It is paperback. I breezed through book with skimreading at first. Simple illustrations with few colours — VERY HELPFUL. The page layout is perfect, very easy on my eyes! I could read quickly, as the text is not too cluttered. I have learned maths fast. Thank you, authors. Moreover, I absolutely love the 4cm margins at the outer edges of pages, as I like pencilling my notes in blank spaces or place sticky notes there. Helpful footnotes in the margins.
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astha malhotra
5.0 out of 5 starsVerified Purchase
Good math book for ml. Comprehensive
Reviewed in the United Kingdom on October 5, 2020
Good for ml enthusiast''s. All the important maths subjects are covered
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Product information

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online

Mathematics for sale Machine lowest Learning online