The book progresses on to discuss these and related issues in the chapters ahead. The book uses the language of graphical models to specify models in a concise and intuitive way. Overviews of real-world applications of various techniques are provided. It is a very well written and engaging book to learn the fundamentals.

Introduction to Machine Learning | BibSonomy

Machine learning is now being used across several sectors including search engines, stock market analysis, DNA sequencing and robot locomotion etc. It is a good comprehensive book which covers basic reasoning to advanced techniques and explains it within the framework of graphical models. Each chapter contains numerous examples and exercises. It is a classic. It covers the still evolving information theory in a very clear way. It delves on the theory of probability from the perspective of information theory.

It has a good instructive mix of mathematics, statistics, physics, and information theory. Important topics such as channel capacity, data compression, entropy, rate distortion, source coding, feedback capacity, hypothesis testing etc. It has very useful problem sets. Each problem points at a new idea mentioned in the text. Telegraphic summary at the end of each chapter and historical notes are helpful. It is considered to be a must read for people who want to get a good grasp of machine learning, data mining and bioinformatics. The concepts are approached and explained in a statistical way.

It deals with many topics like neural network, classification trees, boosting and support vector machines — comprehensively.

It explains additive models and piecewise approaches to linear modelling very well. The section on B-splines is also interesting.

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning)

It lays the conceptual foundations well. This book is a good primer to machine learning. It talks about all the basic concepts that enable the technology to improve its performance with experience accumulated from the observed data. It has all the core topics a student of machine learning should know. The book covers topics like regularization, VC dimension, overfitting, bias and variance in good detail.

The authors have tried to balance theoretical, practical, mathematical and the heuristic. The authors are professors at reputed universities. You will learn how and why things work in a certain way. You will learn to. There is an excellent series of Data Videos lectures machine learning students can watch after reading the book. SVMs or Support Vector Machines is a learning algorithm developed from the results of statistical learning theory.

These SVMs are called Kernals.

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Concept of Kernals gave rise to Kernel machines which used central concepts of SVMs for learning tasks. Kernal machines are replacing neural networks in different fields. This book provides a good introduction to SVMs. The book covers the basics and also documents the research done using Kernals. It equips the reader with basic mathematical knowledge needed to use Kernal algorithms.

This book will enable you to understand and use powerful kernel algorithms to your tasks. This is one book that explains the broad and deep discipline of AI in a simple way. Each part of the book has a non-technical learning material.

The best Machine & Deep Learning books

It has plenty of important topics such as default reasoning and truth maintenance system, multi-agent distributed AI, game theory and detailed probabilistic inference algorithms. It has new set of exercises for students to attempt.


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Very decent introductory book. It gives a very broad overview of the different algorithms and methodologies available in the ML field. Each chapter reads almost independently. It is similar to the Mitchell book but more recent and slightly more math intensive.

Feb 06, Herman Slatman rated it liked it. Little bit hard to get through, but otherwise quite good as an introductory book. You will want to look up stuff after reading this before applying it though. Oct 13, Karidiprashanth rated it really liked it.

Very good for starting. Eren Sezener rated it it was amazing Mar 19, Bharat Gera rated it it was amazing Jan 02, Krysta Bouzek rated it liked it Jun 30, Joel Chartier rated it it was ok Jan 02, Huwenbo Shi rated it liked it Apr 03, Rrrrrron rated it really liked it Apr 07, Ali Ghasempour rated it liked it Nov 03, Teresa Tse rated it it was ok Jul 09, Romann Weber rated it really liked it Sep 04, Nicolas Nicolov rated it it was amazing Jun 21, Alexander Matyasko rated it really liked it May 02, Kaiser rated it liked it Dec 26, Iva Miholic rated it it was amazing Jul 27, Edward McWhirter rated it liked it Feb 14, Mei Carpenter rated it it was amazing Sep 30, Jon rated it really liked it Apr 07, Kanwal Hameed rated it it was amazing Mar 16, Sidharth Shah rated it liked it Oct 22, Jovany Agathe rated it really liked it Nov 22, Ed Hillmann rated it it was ok Nov 10, Omri Cohen rated it really liked it Sep 05, Roberto Salgado rated it really liked it Aug 01, There are no discussion topics on this book yet.

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