000			02292cam a22002538i 4500
001			23694152
005			20250625114650.0
008			240515s2024 mau b 001 0 eng
010			_a 2024017313
020			_a9780262049443
040			_aDLC _beng _erda _cDLC
041			_aeng
042			_apcc
050	0	0	_aQ325.5 _b.B33 2024
100			_aBach, Francis _d1974- _eauthor _925578
245	1	0	_aLearning theory from first principles _c/ Francis Bach.
260			_aCambridge, MA : _bThe MIT Press, _c2024.
300			_a475 pages _billustrations, tables, charts (some color). _c24 cm.
490			_aAdaptive computation and machine learning
504			_aIncludes bibliographical references and index.
520			_aA comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory. Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students. · Provides a balanced and unified treatment of most prevalent machine learning methods · Emphasizes practical application and features only commonly used algorithmic frameworks · Covers modern topics not found in existing texts, such as overparameterized models and structured prediction · Integrates coverage of statistical theory, optimization theory, and approximation theory · Focuses on adaptivity, allowing distinctions between various learning techniques · Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors.
650		0	_aMachine learning _xMathematics _925579
942			_2lcc
999			_c4792 _d4792