Machine Learning: A Bayesian and Optimization Perspective provides a unifying perspective on machine learning by covering both probabilistic and deterministic approaches. Both approaches, which are based on optimization techniques, are used together with the Bayesian inference approach.
This text presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing, and computer science. Emphasis is placed on physical reasoning behind the mathematics. Various methods and techniques are explained in depth, supported by examples and problems which provide an invaluable resource to the student and researcher for understanding and applying machine learning concepts.
The book builds carefully from basic classical methods to the most recent trends, making the text suitable for different courses, including: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.
MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.