Machine Learning & Pattern Recognition Series. Stephen Marsland. A CHAP MAN & HALL BOOK. Page 2. Machine. Learning. An Algorithmic. Perspective. 2nd Edition. Stephen Marsland. Book + eBook $ Series: Chapman & Hall/ CRC Machine Learning & Pattern Recognition. What are VitalSource eBooks?. Machine Learning has ratings and 3 reviews. Traditional books on machine learning can be divided into two groups those aimed at advanced undergraduat.
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All instructor resources are now available on our Instructor Hub. The student resources previously accessed via GarlandScience. What are VitalSource eBooks? For Instructors Request Inspection Copy. Since the best-selling first edition was published, there have been several prominent developments in the field of machine marslaand, including the increasing work on the statistical interpretations of machine learning algorithms.
Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning.
It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Suitable for both an introductory one-semester course and more advanced courses, the text strongly stephwn students to practice with the code. machlne
Machine Learning: An Algorithmic Perspective, Second Edition
Each chapter includes detailed examples along with further reading and problems. Radial Basis Functions and Splines. His research interests in mathematical computing include shape spaces, Euler equations, machine learning, and algorithms. He received a PhD from Manchester University.
I still consider this to be the case. The text, already learninh broad in scope, has been expanded to cover some very relevant modern topics … I highly recommend this text to anyone who wants to learn machine learning … I particularly recommend it to learningg students who have followed along from more of a statistical learning perspective Ng, Hastie, Tibshirani and are looking to broaden their knowledge of applications.
The updated text is very timely, covering topics that are very popular right now and have little coverage in existing texts in this area. This is further highlighted by the extensive use of Python code to implement the algorithms. The topics chosen do reflect the current research areas in ML, and the book can be recommended to those wishing to gain an understanding of the current state of the field.
Hodgson, Computing ReviewsMarch 27, Some of the best features of this book are the inclusion of Python code in the text not just on a websiteexplanation of what the code does, and, in some cases, partial numerical run-throughs of the code.
This helps students understand the algorithms better than high-level descriptions and equations alone and eliminates many sources of ambiguity and misunderstanding.
shephen In each chapter, they will find thorough explanations, figures illustrating the discussed concepts and techniques, lots of programming Python and worked examples, practice questions, further readings, and a support website. The book will also be useful to professionals who can quickly inform and refresh their memory and knowledge stehen how machine learning works and what are the fundamental approaches and methods used in this area.
As a whole, it provides an essential source for machine learning methodologies and techniques, how they work, and what are their application areas. Praise for the First Edition: It includes a basic primer on Python and has an accompanying website.
It has excellent breadth and is comprehensive in terms of the topics it covers, both in terms of methods and in terms of concepts and theory. It would be excellent as a first exposure to the subject, and would put the various ideas in context …” —David J. Hand, International Statistical Review Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on …. This is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.
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Machine Learning: An Algorithmic Perspective by Stephen Marsland
We provide complimentary e-inspection copies of primary textbooks to instructors considering our books for course adoption. Learn More marsand VitalSource Bookshelf. CPD consists of any educational activity which helps to maintain and develop knowledge, problem-solving, and technical skills with the aim to provide better health care through higher standards.
It could be through conference attendance, group discussion or directed reading to name just a marslans examples. We provide a free online form to document your learning and a certificate for your records.
Already read this title? Please accept our apologies for any inconvenience this may cause. Exclusive web offer for individuals. An Algorithmic Perspective, Second Edition. VitalSource eBook access code and instructions will be provided within the print book. Add to Wish List. Toggle navigation Additional Book Information. Summary A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms.
New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code.
Table of Contents Introduction.
Machine Learning: An Algorithmic Perspective
Reviews “I thought the first edition was hands down, one of the best texts covering applied machine learning from mafhine Python perspective. Hodgson, Computing ReviewsMarch 27, “I have been using this textbook for an undergraduate machine learning class for several years. Hand, International Statistical Review78 “If you are interested in ,achine enough AI to understand the sort of new techniques being introduced into Web 2 applications, then this is a good place to start.
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