000			02605nam a22003497a 4500
003			IIITD
005			20240928020004.0
008			231011b xxu||||| |||| 00| 0 eng d
010			_a 2019040762
020			_a9781108470049
040			_aLBSOR/DLC _beng _erda _cDLC _dIIITD
042			_apcc
050	0	0	_aQ325.5 _b.D45 2020
082	0	0	_a510 _223 _bDEI-M
100	1		_aDeisenroth, Marc Peter
245	1	0	_aMathematics for machine learning _cby Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong.
260			_aLondon : _bCambridge University Press, _c©2020
263			_a1912
300			_aiii, 411 p. : _bill. ; _c26 cm.
504			_aThis includes bibliographical references and index.
505	0		_t1. Introduction and motivation _t2. Linear algebra _t3. Analytic geometry _t4. Matrix decompositions _t5. Vector calculus _t6. Probability and distribution _t7. Continuous optimization _t8. When models meet data _t9. Linear regression _t10. Dimensionality reduction with principal component analysis _t11. Density estimation with Gaussian mixture models _t12. Classification with support vector machines
520			_a"The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts"--
650			_aMachine learning
650			_aMachine learning Mathematics
650			_aMathematics
700	1		_aFaisal, A. Aldo
700	1		_aOng, Cheng Soon
776	0	8	_iOnline version: _aDeisenroth, Marc Peter. _tMathematics for machine learning. _dCambridge, United Kingdom ; New York : Cambridge University Press, 2020. _z9781108679930 _w(DLC) 2019040763
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _07
999			_c171813 _d171813