Dr. Li Hang’s Statistical Learning Methods can be said to be an introduction to machine learning. Now, statistical learning methods (the second edition) was published in May this year, on the basis of the first edition of supervised learning, added unsupervised learning content, more rich, is very worth learning materials. Recently, Professor Yuan Chun of Shenzhen Research Institute of Tsinghua University made a courseware for the convenience of studying and watching. Dr. Li Hang is hereby publicized on weibo.



expericnce, male, graduated from the Department of Electrical and Electronic Engineering, Kyoto University, Japan, and obtained a doctor’s degree in computer science from Tokyo University, Japan. Adjunct professor at Peking University and Nanjing University. He used to be a researcher of NEC Central Research Institute, Japan, senior researcher and principal researcher of Microsoft Research Asia, and director of Noah’s Ark Laboratory of Huawei Technologies Co., LTD. Currently, he is the director of artificial Intelligence Lab of Bytedance Technology Co., LTD. His research interests include natural language processing, information retrieval and machine learning.


Content abstract

Statistical learning method, namely machine learning method, is an important subject in the field of computer and its application. This book is divided into supervised learning and unsupervised learning. It comprehensively and systematically introduces the main methods of statistical learning, including perceptron, K-nearest neighbor method, Naive Bayes method, decision tree, Logistic Regression and maximum entropy model, support vector machine, lifting method, EM algorithm, hidden Markov model and conditional random field. And clustering methods, singular value decomposition, principal component analysis, latent semantic analysis, probabilistic latent semantic analysis, Markov chain Monte Carlo method, latent Dirichlet assignment and PageRank algorithm.
Each chapter introduces a method, except for four chapters on statistical, supervised, and unsupervised learning, which provide an overview and summary. The narration tries to start with specific problems or examples, from the simple to the deep, clarify ideas, give the necessary mathematical derivation, so that readers can master the essence of statistical learning methods and learn to use them. In order to meet the needs of readers for further study, the book also introduces some relevant research, gives a few exercises, and lists the main references. This book is a teaching reference book for statistical machine learning and related courses. It is suitable for college students and graduate students majoring in text data mining, information retrieval and natural language processing, as well as for r & D personnel engaged in computer application.


The courseware




directory

The second edition of Statistical Learning Methods, which is divided into two parts, is now available for pre-order on platforms such as JD.com and Taobao. The first part of supervised learning is basically the same as the first edition in terms of content and theme, and only the big chapter titles are shown here. Unsupervised learning in Part 2 is completely new, so more details are shown here.


Supervised Learning

Chapter 1 introduction to statistical learning and supervised learning

Chapter 2 perceptron
Chapter 3 nearest neighbor method
Chapter 4 naive Bayes method
Chapter 5 decision tree
Chapter 6: Logistic Logistic Regression and maximum entropy model
Chapter 7 Support vector machines
Chapter 8 Methods of promotion
Chapter 9 EM algorithm and its extension
Chapter 10 Hidden Markov model
Chapter 11 conditional random airport
Chapter 12 summarizes the methods of supervised learning


Unsupervised learning

Chapter 13 introduction to unsupervised learning
Chapter 14 clustering method
Chapter 15 singular value decomposition
Chapter 16 principal component analysis
Chapter 17 latent semantic analysis
Chapter 18 probability latent semantic analysis
Chapter 19 markov chain Monte Carlo method
Chapter 20 potential Dirichlet allocation
Chapter 21 PageRank algorithm
Chapter 22 summary of unsupervised learning methods


Appendix A Gradient descent method
Appendix B Newton’s method and quasi-Newton’s method
Appendix C Lagrange duality
Appendix D Basic subspaces of matrices
Appendix E Definition of KL divergence and properties of dirichlet distribution
The index


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