About the Book 

This book is the second edition of the linear algebra and optimization book that was published in 2020. The exposition in this book is greatly simplified as compared to the first edition. The second edition is enhanced with a large number of solved examples and exercises. T This book teaches linear algebra and optimization in a manner that is specifically focused on machine learning. Therefore, the book also provides significant exposure to machine learning. The chapters of this book belong to two categories: 

1. Linear algebra and its applications: These chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection.

2. Optimization and its applications: Basic methods in optimization such as gradient descent, Newton’s method, and coordinate descent are discussed. Constrained optimization methods are introduced as well. Machine learning applications such as linear regression, SVMs, logistic regression, matrix factorization, recommender systems, and K-means clustering are discussed in detail. A general view of optimization in computational graphs is discussed together with its applications to backpropagation in neural networks. 

The book contains 760 examples and exercises, of which 430 are solved examples/exercises. The book has been written for a diverse audience, including graduate students, researchers, and practitioners. 

About the Author 

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 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.”