Machine Learning

Zhi-Hua Zhou is a leading expert on machine learning and artificial intelligence. He is currently a Professor, Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and the founding director of the LAMDA Group at Nanjing University, China. Prof. Zhou has authored the books "Ensemble Methods: Foundations and Algorithms" (2012) and "Machine Learning" (in Chinese, 2016), and published more than 200 papers in top-tier international journals and conferences. He founded the ACML (Asian Conference on Machine Learning), and served as chairperson for many prestigious conferences, including AAAI 2019 program chair, ICDM 2016 general chair, IJCAI 2015 machine learning track chair, and area chair for NeurIPS, ICML, AAAI, IJCAI, KDD, etc. He is editor-in-chief of Frontiers of Computer Science, and has been an associate editor for prestigious journals such as the Machine Learning journal and IEEE PAMI.  He is a Fellow of the ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, CCF and CAAI, and recipient of numerous awards, including the National Natural Science Award of China and the IEEE Computer Society Edward J. McCluskey Technical Achievement Award.

“The book is full of cross-references, making the reader well aware of tight interconnections between many of the different approaches and methods. … the book is written in a very comprehensible and readable way. Its comprehensibility is further encreased through frequent marginal notes and through consistently illustrating all presented kinds of methods using the same toy example, and through historical notes to all addressed areas … the book explains also several quite advanced subjects … .” (Martin Holeňa, zbMATH 1479.68001, 2022)

• 1 Introduction.- 2 Model Selection and Evaluation.- 3 Linear Models.- 4 Decision Trees.- 5 Neural Networks.- 6 Support Vector Machine.- 7 Bayes Classifiers.- 8 Ensemble Learning.- 9 Clustering.- 10 Dimensionality Reduction and Metric Learning.- 11 Feature Selection and Sparse Learning.- 12 Computational Learning Theory.- 13 Semi-Supervised Learning.- 14 Probabilistic Graphical Models.- 15 Rule Learning.- 16 Reinforcement Learning.