000			01959cam a22002535i 4500
005			20250630164515.0
008			240130s2024 mau 000 0 eng
020			_a9783111288475
040			_aCSL _cCSL
041			_2eng _aeng
084			_aD65,8(B):(S:72) R4 _qCSL
100	1		_aVeiga, Maria Han _eauthor. _9814573
245	1	4	_aThe Mathematics of Machine Learning _b: Lectures on Supervised Methods and Beyond
260			_aBoston : _bDe Gruyter, _c2024.
300			_aix, 199p. _b: col. ill. _c; 24 cm.
500			_aIncludes Bibliography and Index.
520			_aThis book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics.There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction.This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
650			_aStatistical learning theory. _9814574
650			_aAlgorithms.
650			_aKernel methods. _9814504
650			_aMachine learning. _9480917
700	1		_aGed, François Gaston _eco-author. _9814575
942			_2CC _n0 _cTEXL _e2nd ed. _hD65,8(B):(S:72) R4
999			_c1432935 _d1432935