Machine Learning, Revised and Updated Edition
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- Type：Epub+TxT+PDF+Mobi
- Create Date：2021-10-05 06:51:40
- Update Date：2025-09-07
- Status：finish
- Author：Ethem Alpaydin
- ISBN：0262542528
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Summary
A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars。
Today， machine learning underlies a range of applications we use every day， from product recommendations to voice recognition--as well as some we don't yet use everyday， including driverless cars。 It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem。 In this volume in the MIT Press Essential Knowledge series， Ethem Alpaydin offers a concise and accessible overview of the new AI。 This expanded edition offers new material on such challenges facing machine learning as privacy， security， accountability， and bias。
Alpaydin， author of a popular textbook on machine learning， explains that as Big Data has gotten bigger， the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced。 He describes the evolution of the field， explains important learning algorithms， and presents example applications。 He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning， when an autonomous agent learns to take actions to maximize reward。 In a new chapter， he considers transparency， explainability， and fairness， and the ethical and legal implications of making decisions based on data。
Today， machine learning underlies a range of applications we use every day， from product recommendations to voice recognition--as well as some we don't yet use everyday， including driverless cars。 It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem。 In this volume in the MIT Press Essential Knowledge series， Ethem Alpaydin offers a concise and accessible overview of the new AI。 This expanded edition offers new material on such challenges facing machine learning as privacy， security， accountability， and bias。
Alpaydin， author of a popular textbook on machine learning， explains that as Big Data has gotten bigger， the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced。 He describes the evolution of the field， explains important learning algorithms， and presents example applications。 He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning， when an autonomous agent learns to take actions to maximize reward。 In a new chapter， he considers transparency， explainability， and fairness， and the ethical and legal implications of making decisions based on data。
Reviews
Asli
，
Yapay zeka， yapay sinir ağları， derin öğrenme gibi konulara ilgi duyanlar için iyi bir temel kitabı， konuyu mümkün olduğunca sade bir dille anlatmaya çalışmış， çok kolay okunuyor diyemeyeceğim ancak beraberinde internet araştırmalarıyla okuma çeşitlendirilince oldukça öğretici。
Anthony O'Connor
，
Good introductionA very brief but interesting and thorough enough introduction to the field。 It manages to convey the fields own structures and patterns in a way superior to any of the learning algorithms 。。。 yet。 The author is a little to quick to dismiss the dangers of strong AI based on machine learning - or otherwise。 There are different points of view on this all worth considering。 But he brushes it all off in a paragraph。
Jessica Lazar
，
The content of each topic seemed both shallow and in depth at the same time， making it difficult to read and fully understand these concepts。 However， the book's final chapter put everything in a clear way， letting me take the key points from this book。 The content of each topic seemed both shallow and in depth at the same time， making it difficult to read and fully understand these concepts。 However， the book's final chapter put everything in a clear way， letting me take the key points from this book。 。。。more
Utku Sakallıoğlu
，
Kötü çeviri。
Shraddha Jadhav
，
A good book for a beginner who wants to learn about basic terminologies of Machine Learning， Deep Learning， and Data Science。 This book contains simple information no code snippets or mathematical formulae。 Worth a read to pass time。
Gul Bulut
，
Kitabın ingilizce baskısını da edinmiştim。 Çevrilmesi Türkçe literatüre çok önemli bir kazanım。 Overfit ile kanizsa üçgeni arasında bende bir bağ kurdu kitap ve bunun gibi bir çok kapı açtı。
Hernán Borré
，
Not the best for newbies but very nice for post graduate studies or reasearch
Maryam Taheri
，
یک مرجع عالی برای یادگیری ماشین که از صفر تا صد همهی بخشهاش رو با بیان خیلی خیلی ساده گفته و تمام چیزی که توی هر بخش لازمه رومیگه و هیچچیز رو از قلم نمیندازه。
Mark Higley
，
I had to force myself to finish it。 But that reflects more on my ADD than the content of the book。 It is a well written summary of the concept of machine learning and AI。 If you're into computers and the future of technology， give it a read。 I had to force myself to finish it。 But that reflects more on my ADD than the content of the book。 It is a well written summary of the concept of machine learning and AI。 If you're into computers and the future of technology， give it a read。 。。。more
Robinsky_
，
puh also echt weniiiiig kritik， eher so für business leute die ~ inspired 🤩~ werden sollen， aber netter basis einblick in viele konzepte， leider null tiefergehend erklärt deswegen echt najaaa 2，5 sterne
Rick Sam
，
A Primer for Machine Learning。 If you are an undergrad or want to know broad topics in Machine Learning， I'd recommend you this book。 This could be used as the first gentle introduction， perhaps for 5th grade-10th grade。 Deus Vult，Gottfried。 A Primer for Machine Learning。 If you are an undergrad or want to know broad topics in Machine Learning， I'd recommend you this book。 This could be used as the first gentle introduction， perhaps for 5th grade-10th grade。 Deus Vult，Gottfried。 。。。more
Kieran Wood
，
This is a great introduction to machine learning， and a useful dictionary of sorts。 Having every form explained in the chronology presented gives you a good idea of why different algorithms were implemented at different points， and what shortcomings they both resolve and present。Well worth a read as an introduction to machine learning。
An Te
，
A helpful primer on machine learning。 A near-complete introduction to the subject of machine learning from its applications， theories， tools， manifestations and its possible future。 Some material is a rehash of his book "Data Science。"For a reader， one would be aware of the recommendations that come our way from websites/retailers， all generated from machine learning methods， as they may simply be reinforcing your preferences as we speak。 The future methods will hope to add more diversity to our A helpful primer on machine learning。 A near-complete introduction to the subject of machine learning from its applications， theories， tools， manifestations and its possible future。 Some material is a rehash of his book "Data Science。"For a reader， one would be aware of the recommendations that come our way from websites/retailers， all generated from machine learning methods， as they may simply be reinforcing your preferences as we speak。 The future methods will hope to add more diversity to our recommendations。 It must be noted also， the book is pro-machine learning but many will still desire the privacy that a world governed by machine learning will certainly jeopardise or preclude entirely。 The digital age has changed some of the infrastructure on how we connect， for good or ill。 Machine learning would best be equipped by the devout and the tech-savvy。 That much was lacking in this book。 But on the whole， it covered all the technical ground。 。。。more
Jay
，
I found this a good re-introduction to machine learning。 By re-introduction， I’m using my perspective based on experience， having worked in AI and neural nets twenty years back， but keeping up through pop science magazine articles and such since then。 For me， I was reminded of many of the methods I knew， and a few I hadn’t heard of。 Short。 Nice description， just what you want。
Michael Messinger
，
Very good intro to the field。 Succinct and well-organized。 Nice feature of highlighting key terms。 Provides good definitions and examples in explanations。
Colin Thomson
，
This was an interesting and broad introduction to the topic of machine learning。 It was accessible enough not to feel overwhelmed， and managed not to bombard you with technical terms。 I read this for professional development， and feel I reaped the necessary benefits。 Given I normally read for pleasure， I'm not sure how to rate this。 Maybe it's 4 stars？！？！ Regardless， I'd recommend it to anyone in tech。 This was an interesting and broad introduction to the topic of machine learning。 It was accessible enough not to feel overwhelmed， and managed not to bombard you with technical terms。 I read this for professional development， and feel I reaped the necessary benefits。 Given I normally read for pleasure， I'm not sure how to rate this。 Maybe it's 4 stars？！？！ Regardless， I'd recommend it to anyone in tech。 。。。more
Philip Weiss
，
This book is for the person who wants an introduction to a whole collection of technologies。 The author is descriptive， informative， and very clear。The author avoids going into too many details， so this is not the book for someone to learn how to implement the ideas。 The theme is more targeted to the reader who wants to learn the terminology， the background， and the uses of the various machine learning techniques。
Danny Moril-Las
，
Very important to note that this book is a very basic introduction to Machine Learning。 If you already work with ML， if you already have experience， if you are advanced。。。 this is not your book。It's not a technical book and it explains the main and basic concepts of Machine Learning。 If you are now getting into ML and you want an introduction to some wording and concepts this is an interesting and helpful book。 Very important to note that this book is a very basic introduction to Machine Learning。 If you already work with ML， if you already have experience， if you are advanced。。。 this is not your book。It's not a technical book and it explains the main and basic concepts of Machine Learning。 If you are now getting into ML and you want an introduction to some wording and concepts this is an interesting and helpful book。 。。。more
Stefan Kanev
，
This is a curious book。I'm mostly grateful that it introduced me to the MIT Essential Knowledge series， which seem pretty promising。 They are short， small and beautifully produced。 I ordered a bunch of others and am quite keen to start reading them。That being said， this book had a lot of promise， but I don't feel it delivered fully。 It's a very high-level overview of what Machine Learning is and not much more。 It doesn't go to code （or even math）， but uses well-written prose to explain each key This is a curious book。I'm mostly grateful that it introduced me to the MIT Essential Knowledge series， which seem pretty promising。 They are short， small and beautifully produced。 I ordered a bunch of others and am quite keen to start reading them。That being said， this book had a lot of promise， but I don't feel it delivered fully。 It's a very high-level overview of what Machine Learning is and not much more。 It doesn't go to code （or even math）， but uses well-written prose to explain each key idea that you will encounter in a detail ML course。 It's not incredibly well-structured， and it does not attempt to present the material in a good hierarchical way。 You have to read carefully and pay a lot of attention， otherwise it's easy to get lost。 The author shares some high-level thoughts about automation and where the world is going with it， some of which very insightful， some of which a bit out there。That being said， I would still recommend it。 It's mostly a "non-technical CEO" book – if you want to get a high-level sense of what ML is， without going in the weeds， it's worth checking out。 Even better if you like beautiful books。 。。。more
Selcuk
，
It gives some basic and inclusionary information about machine learning。 It has a simple language to read。
ⵎⵓⵏⵉⵔ
，
This is a good introductory book to Machine Learning and related topics （Data Analysis， Artificial Intelligence， etc）， for those who might want to eventually deepen their technical knowledge of ML， without having to gruel through the details of the methods and algorithms from the get-go。 It may also be a good read for non-technical people who have deep interest in the subject， and would not mind grappling with some of the advanced jargon in the domains of statistics and computer science。
Grouchomax
，
This book lacks of a proper structure to follow the overview on machine learning tools and applications: I thought it was supposed to help neophytes but it seems not written in pedagogic terms。 A pity to say that the most useful part is the Glossary at the end: consider it a sort of summary of the book's content and the main long-term take away。 This book lacks of a proper structure to follow the overview on machine learning tools and applications: I thought it was supposed to help neophytes but it seems not written in pedagogic terms。 A pity to say that the most useful part is the Glossary at the end: consider it a sort of summary of the book's content and the main long-term take away。 。。。more
Grace Scott
，
Good overview of history， applications， and future directions of AI。
Tabrez Shaikh
，
Good conceptual understanding of machine learning and different techniques for the same。
Antoine Balaine
，
I have seldom ever read a book which's introduction's condescending tone so strongly insulted my intelligence， to then just throw muddy information in every direction， without bothering to clearly hierarchize details from core concepts。 This author plowed through the writing in a rush， with zero empathy for his reader。 I have seldom ever read a book which's introduction's condescending tone so strongly insulted my intelligence， to then just throw muddy information in every direction， without bothering to clearly hierarchize details from core concepts。 This author plowed through the writing in a rush， with zero empathy for his reader。 。。。more
Kevin McDonagh
，
A rather dry primer of high level ML concepts。
Olatomiwa Bifarin
，
Great introduction to Machine Learning。 To read through seamlessly， some very basic knowledge of CS and stats would help a great deal。 Having taken a graduate level class in statistical learning this was a light read， except for a few sections on deep learning。 My favorite parts of the book are Ethem's salutary use of analogies like， 'learning with a critic' for reinforcement learning; 'learning with a teacher' for supervised learning; 'learning without a teacher' for unsupervised learning; and Great introduction to Machine Learning。 To read through seamlessly， some very basic knowledge of CS and stats would help a great deal。 Having taken a graduate level class in statistical learning this was a light read， except for a few sections on deep learning。 My favorite parts of the book are Ethem's salutary use of analogies like， 'learning with a critic' for reinforcement learning; 'learning with a teacher' for supervised learning; 'learning without a teacher' for unsupervised learning; and a whole lot of those handy goodies。 In general， this is a little nice book on a technology that is rapidly changing the way we live！ From digital medical diagnosis， to Alexa， to amazon go。 。。。more
Antonis Maronikolakis
，
Machine Learning by Ethem Alpaydin is a short book on Machine Learning。 It serves as an introduction to the field， explaining in a nutshell the different techniques and algorithms in Machine Learning。 The author takes great care to bring forward the concepts in a simple manner so that newcomers to the field can get a taste of what to expect。It does not go in depth on the specifics and it doesn’t introduce the mathematical background of the field， but it gives enough to entice and give a quick ov Machine Learning by Ethem Alpaydin is a short book on Machine Learning。 It serves as an introduction to the field， explaining in a nutshell the different techniques and algorithms in Machine Learning。 The author takes great care to bring forward the concepts in a simple manner so that newcomers to the field can get a taste of what to expect。It does not go in depth on the specifics and it doesn’t introduce the mathematical background of the field， but it gives enough to entice and give a quick overview to the reader。The book is not to be used as a textbook， but as a quick read on Machine Learning。 Ideal for someone who wants to quickly get the gist of the field， or to someone needing an introduction。 。。。more
Alessandra Zanetti
，
Interesting as a quick quick read， but I was hoping for more examples on how the learning mechanism works， and specific applications。
James
，