000			02207nam a22002657a 4500
005			20220512160032.0
008			220512b |||||||| |||| 00| 0 eng d
020			_a9788120350786
028			_a:Donated from Prof Madan Gopal
082			_a006.31 ALP
100			_aAlpaydin, Ethem
245			_aIntroduction to Machine Learning _c/ Ethem Alpaydin
260			_aCambridge, Massachusetts _b: The MIT Press, _c©2014
300			_axxii, 613 pages _c24cm.
365			_bDonated
505			_a 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.
520			_aMachine 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. --
650			_aComputer science,
650			_aSpecial computer methods (e.g. AI, multimedia, VR)[4]
650			_aMachine learning.
650			_aApprentissage automatique.
650			_aComputers - General.
700			_aAlpaydin, Ethem
942			_cMDL
999			_c59215 _d59215