Machine learning refined: foundations, algorithms, and applications
Watt, J. Borhani
Machine learning refined: foundations, algorithms, and applications - 2nd Ed. - New York: Cambridge University Press, 2020. - xxi, 574 p. hb; 25 cm.
The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more
9781108480727
Machine Learning
Optimization
Linear Learning
Nonlinear Learning
006.31 / WAT
Machine learning refined: foundations, algorithms, and applications - 2nd Ed. - New York: Cambridge University Press, 2020. - xxi, 574 p. hb; 25 cm.
The second edition of this text is a complete revision of our first endeavor, with virtually every chapter of the original rewritten from the ground up and eight new chapters of material added, doubling the size of the first edition. Topics from the first edition, from expositions on gradient descent to those on One-versus- All classification and Principal Component Analysis have been reworked and polished. A swath of new topics have been added throughout the text, from derivative-free optimization to weighted supervised learning, feature selection, nonlinear feature engineering, boosting-based cross-validation, and more
9781108480727
Machine Learning
Optimization
Linear Learning
Nonlinear Learning
006.31 / WAT
