Description
Springer Machine Learning by Zhou Zhi-Hua
Machine Learning a vital and core area of artificial intelligence (AI) is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts Part 1 (Chapters 1-3) introduces the fundamentals of machine learning including terminology basic principles evaluation and linear models Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods such as decision trees neural networks support vector machines Bayesian classifiers ensemble methods clustering dimension reduction and metric learning Part 3 (Chapters 11-16) introduces some advanced topics covering feature selection and sparse learning computational learning theory semi-supervised learning probabilistic graphical models rule learning and reinforcement learning. Each chapter includes exercises and further reading so that readers can explore areas of interest.The book can be used as an undergraduate or postgraduate textbook for computer science computer engineering electrical engineering data science and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.