Fundamentals of Machine Learning

Thomas Trappenberg

ISBN 10: 0198828047 / ISBN 13: 9780198828044

Published by OXFORD UNIV PR, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.

Seller Inventory # 9780198828044

Bibliographic Details

• Title: Fundamentals of Machine Learning
• Publisher: OXFORD UNIV PR
• Publication Date: 2020
• Language: English
• ISBN 10: 0198828047
• ISBN 13: 9780198828044
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 246W x 189H x 15D millimeters 246 millimeters width by 189 millimeters height by 15 millimeters depth
• Weight: 510 grams

About this title

Synopsis

Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life.

This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning.

Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.

About the Author

Thomas Trappenberg, Professor of Computer Science, Dalhousie University

Dr. Trappenberg is a professor of Computer Science at Dalhousie University. He holds a PhD in physics from RWTH Aachen University and held research positions in Canada, Riken Japan, and Oxford England. His main research areas are computational neuroscience, machine learning and robotics. He is the author of Fundamental of Computational Neuroscience and the cofounder of Nexus Robotics and ReelData. He is currently working on applying AI to several other areas in the food industry and in medical applications.