Linear algebra and optimization for machine learning

A frequent challenge faced by beginners in machine learning is the extensive background requireeent in linear algebra and optimization. This makes the learning curve very steep. Thisbpok. therefore, reverses the focus by teaching linear algebra and optimization asthe priery topics of interest, and solutions to machine learning problems as applications of the a methods. 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.
Exercises are included both within the text of the chapters and at the end of the chapters. The book is written fora diverse audience, including graduate students, researchers, and practitioners.

Source : Springer