This tutorial text offers a comprehensive perspective on machine learning, integrating both probabilistic and deterministic approaches, rooted in optimization techniques, alongside Bayesian inference principles utilizing a hierarchy of probabilistic models. The book systematically introduces major machine learning methods developed across various disciplines such as statistics, statistical and adaptive signal processing, and computer science. Emphasizing the underlying physical reasoning behind mathematical concepts, each method is thoroughly explained with examples and problems, providing a valuable resource for students and researchers seeking a deep understanding and practical application of machine learning concepts.
The content progresses from fundamental classical methods to cutting-edge trends, with chapters designed to be as self-contained as possible, catering to diverse courses including pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, and short courses on sparse modeling, deep learning, and probabilistic graphical models.
Key Features:
- Coverage of major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression, and boosting methods.
- Exploration of the latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning, and latent variables modeling.
- Case studies illustrate real-world applications such as protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization, and echo cancellation.
- MATLAB code for the main algorithms is provided on an accompanying website, allowing readers to experiment with the code and enhance their practical understanding.
Download
See also
- Software Exorcism: A Handbook for Debugging and Optimizing Legacy Code by Bill Blunden (2003)
- Hormones and the Endocrine System: Textbook of Endocrinology by Bernhard Kleine (2016)
- Genetics: A Conceptual Approach 6e by Benjamin A. Pierce (2017)
- A Functional Approach to Java by Ben Weidig (2022)
- Calculus of variations and optimal control by Amol Sasan (2005)