000			01280 a2200157 4500
020			_a9780262542524
082			_a006.31 ALP
100			_aAlpaydin, Ethem
245			_aMachine Learning
260			_bMIT Pres _aCambridge _c2021
300			_a255
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			_aMachine Learning
650			_aArtificial Intelligence
942			_cBK _2ddc
999			_c44305 _d44305