000			02267nam a2200205Ia 4500
005			20250408173457.0
008			230421s2020||||xx |||||||||||||| ||eng||
020			_a9780198828044
041			_aEnglish
082			_a631 T73
100			_aTrappenberg, P. Thomas _eAuthor _9886
245		0	_aFundamentals of machine learning
260			_aUnited Kingdom: _bOxford University Press, _c2020.
300			_avii, 260p.; 21cms.
500			_a 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.
650			_aFundamentals of Machine Learning _96091
650			_aComputer science and computational neuroscience _96092
650			_aIntroduction to Bayesian approaches to modeling _96093
942			_cBK
999			_c796 _d796