000			01391nam a2200169Ia 4500
008			210916s9999 xx 000 0 und d
020			_a9788120350786 (pbk.)
082			_a006.31 _bALP
100			_aAlpaydin, Ethem _97059
245		0	_aIntroduction to machine learning
250			_a3rd ed.
260			_aDelhi _bPHI Learning Pvt Ltd _c2020
300			_axxii, 613p.
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 perceptron's and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptron's; 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. -- Edited summary from book
650			_aMachine learning _9481
942			_cBK
999			_c7031 _d7031