Solution Manual for Machine Learning: A Bayesian and Optimization Perspective – 1st and 2nd Edition
+ Textbook for 2nd and 1st Edition
Author: Sergios Theodoridis
The Textbook and Solution Manual for Machine Learning by Theodoridis are sold separately. You can contact us if you have any questions.
First product includes two official Solution Manuals for 1st and 2nd edition. Solution Manual for 1st Edition covers all chapters 2 to 18 of textbook (except than chapters 11). Also, Solution Manual for 2nd edition covers all chapter of the textbook (chapters 2 to 19). There is one PDF file for each of chapters on both of editions.
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Second product includes two textbooks for 1st and 2nd Editions. Their specifications and the covers are available in following.
About the textbook: Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Read more
File Specification for Textbook 2nd Edition
File Specification for Textbook 1st Edition
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