02207nam a22002657a 4500 20220512160032.0 220512b |||||||| |||| 00| 0 eng d 9788120350786 :Donated from Prof Madan Gopal 006.31 ALP Alpaydin, Ethem Introduction to Machine Learning / Ethem Alpaydin Cambridge, Massachusetts : The MIT Press, ©2014 xxii, 613 pages 24cm. Donated Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of this title reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods. -- Computer science, Special computer methods (e.g. AI, multimedia, VR)[4] Machine learning. Apprentissage automatique. Computers - General. Alpaydin, Ethem MDL 59215 59215 0 0 ddc 0 0 MDL MDL 2022-05-12 Donated from Prof Madan Gopal 006.31 ALP M375 2022-05-12 00:00:00 2022-05-12 MDL