Binding: Paperback
Description: 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.
Title: Fundamentals Of Machine Learning
Author(s): Trappenberg Thomas P. (Professor Of Computer Science Professor Of Computer Science Dalhousie University)
Publisher: Oxford University Press
Barcode: 9780198828044
Pages: 260 Pages
Publication Date: 1/28/2020
Category: Machine Learning