现货包邮 机器学习:贝叶斯和优化方法(英文版 原书第2版)|8076676

Machine Learning: Bayesian and Optimization Methods (2nd Edition) is an essential guide for anyone interested in the field of machine learning. The book unveils a unified perspective of machine learning by discussing the two pillars of supervised learning: regression and classification. The author covers fundamental concepts such as least squares, maximum likelihood, Bayesian decision theory, logistic regression, and decision trees, and proceeds to introduce advanced topics like Monte Carlo methods, particle filtering, and probabilistic graphical models.The book also highlights the importance of optimization in machine learning and covers the basics of convexity and convex optimization, including one chapter on stochastic approximation and the family of gradient descent algorithms. The author concludes the book with an extension chapter on neural networks and deep learning architectures, which makes it current and practical.The book is well-written and easy to understand, making it accessible to readers of all levels. The examples and explanations are clear, and the exercises throughout the book provide an opportunity for readers to put the concepts they have learned into practice.Overall, Machine Learning: Bayesian and Optimization Methods (2nd Edition) is highly recommended to anyone who wants to gain a comprehensive understanding of the foundations, concepts, and advanced techniques of machine learning and optimization. It is a must-read for students, researchers, and anyone working in machine learning, data mining, and artificial intelligence.