Probability and Statistics for Machine Learning

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 400 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 20 books, including textbooks on linear algebra, machine learning, neural networks, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several awards, including the EDBT Test-of-Time Award (2014), the ACM SIGKDD Innovation Award (2019), the IEEE ICDM Research Contributions Award (2015), and the IIT Kanpur Distinguished Alumnus Award (2023).He is also a recipient of the W. Wallace McDowell Award, the highest award given solely by the IEEE Computer Society across the field of computer science. He has served as an editor-in-chief of ACM Books and is currently serving as an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He is a fellow of the SIAM, ACM, and the IEEE, for“contributions to knowledge discovery and data mining algorithms.”

• Chapter. 1. Probability and Statistics: An Introduction.- Chapter. 2. Summarizing and Visualizing Data.- Chapter. 3. Probability Basics and Random Variables.- Chapter. 4. Probability Distributions.- Chapter. 5. Hypothesis Testing and Confidence Intervals.- Chapter. 6. Reconstructing Probability Distributions from Data.- Chapter. 7. Regression.- Chapter. 8. Classification: A Probabilistic View.- Chapter. 9. Unsupervised Learning: A Probabilistic View.- Chapter. 10. Discrete State Markov Processes.- Chapter. 11. Probabilistic Inequalities and Extreme Value Analysis.- Bibliography.- Index.