Introduction to Machine Learning (Adaptive Computation and Machine Learning series) [Ethem Alpaydin] on *FREE* shipping on qualifying offers. Introduction to Machine Learning has ratings and 11 reviews. Rrrrrron said: Easy and straightforward read so far (page ). However I have a rounded. I think, this book is a great introduction to machine learning for people who do not have good mathematical or statistical background. Of course, I didn’t.
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Nov 04, Sunita Thapa rated it really liked it. You can see all editions from here. Books by Ethem Alpaydin. To see what your friends thought of this book, please sign up.
Machine Learning Textbook: Introduction to Machine Learning (Ethem ALPAYDIN)
New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web with downloadable results for instructors ; and many additional exercises.
More of a “physical” treatment. Eren Sezener rated it it was amazing Mar 19, Bharat Gera rated it it was amazing Jan 02, Other books in the series. Want to Read saving…. See Mitchell, ; Russell and Norvig; The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning.
Very good for starting. I felt this was a good introduction to machine learning without being overly technical. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions.
Very decent introductory book.
Machine Learning by Ethem Alpaydin
This was a short book and I did not enjoy it. Goodreads helps you keep track of books you learnimg to read. If you are after learning about the algorithms or specifics of how machine learning works, you will likely be disappointed which, admittedly, was my reaction because of my expectations and goals. Just the al;aydin book to get a wide and shallow picture of all the topics concerned with data manipultation: Hardly qualify Essential Knowledge, better to read Wikipedia.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem.
Not super insightful and some of the explanations are so vague or vaguely worded as to not convey much information at all, but that’s what happens when you’re writing a non-technical survey of a technical space.
I listened to tto as an audiobook. A great read nontheless. Really knew all this topics, but the book helped me arrange learhing concepts I had mixed up a bit. See 2 questions about Introduction to Machine Learning….
Introduction to Machine Learning
Oct 01, Arkajit Dey rated it it was amazing. But once that part has past, the author Alpaydin explains the conceptual ideas behind the algorithms and the thinking surrounding Machine Learning, AI and neural networks. I found issue with the mixing of important concepts with unimportant ones to the point which the big ideas are not presented clearly. The denominator should be divided by N inside sqrt: It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
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No math or code, but manages to convey the basic ideas behind fundamental ML algorithms from linear regressions to neural networks. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts.
Was goed, maar te weinig diepgaand. Mei Carpenter rated it it was amazing Sep 30, Oct 13, Karidiprashanth rated it really liked it. Dec 15, Stephen rated it it was ok.
Just a moment while we sign you in to your Goodreads account. Second line of Eq. After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.
Table of Contents and Sample Chapters. But of course, for the doers, going to fx. Lists with This Book.