Machine Learning: A Bayesian and Optimization Perspective (Net Developers)

The publication builds cautiously from the essential classical methods to  the newest trends, with chapters created to become as self-contained as feasible, making the text ideal for  different courses: design recognition, statistical/adaptive signal digesting, statistical/Bayesian learning, in addition to short classes on sparse modeling, deep learning, and probabilistic graphical versions.

• All main classical methods: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and on the web learning, Bayesian classification, decision trees, logistic regression and improving strategies. Concentrating on the physical reasoning behind the mathematics, all of the various strategies and methods are explained comprehensive, supported by illustrations and problems, providing a great resource to the college student and researcher for understanding and applying machine learning ideas.
• Case studies - proteins folding prediction, optical personality recognition, textual content authorship identification, fMRI data evaluation, change point recognition, hyperspectral image unmixing, focus on localization, channel equalization and echo cancellation, show the way the theory could be applied. The publication presents the main machine learning methods because they have been created in different disciplines, such as for example stats, statistical and adaptive signal digesting and computer technology.

See also

• Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development (Prentice-Hall Signal Processing Series)
• Time Frequency Analysis: Theory and Applications
• DSP First (2nd Edition)