LEADER 03320nam 22006615 450 001 9910857794803321 005 20250807135728.0 010 $a3-031-51822-5 024 7 $a10.1007/978-3-031-51822-5 035 $a(MiAaPQ)EBC31337740 035 $a(Au-PeEL)EBL31337740 035 $a(CKB)31995366000041 035 $a(DE-He213)978-3-031-51822-5 035 $a(EXLCZ)9931995366000041 100 $a20240510d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aExercises in Applied Mathematics $eWith a View toward Information Theory, Machine Learning, Wavelets, and Statistical Physics /$fby Daniel Alpay 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2024. 215 $a1 online resource (0 pages) 225 1 $aChapman Mathematical Notes,$x3005-1517 311 08$a3-031-51821-7 327 $aPrologue -- Part I: Algebra -- Linear Algebra -- Positive Matrices -- Algebra and Error Correcting Codes -- Part II: Analysis -- Complements in Real and Complex Analysis -- Complements in Functional Analysis -- Part III: Probability and Applications -- Probability Theory -- Entropy: Discrete Case -- Thermodynamics. 330 $aThis text presents a collection of mathematical exercises with the aim of guiding readers to study topics in statistical physics, equilibrium thermodynamics, information theory, and their various connections. It explores essential tools from linear algebra, elementary functional analysis, and probability theory in detail and demonstrates their applications in topics such as entropy, machine learning, error-correcting codes, and quantum channels. The theory of communication and signal theory are also in the background, and many exercises have been chosen from the theory of wavelets and machine learning. Exercises are selected from a number of different domains, both theoretical and more applied. Notes and other remarks provide motivation for the exercises, and hints and full solutions are given for many. For senior undergraduate and beginning graduate students majoring in mathematics, physics, or engineering, this text will serve as a valuable guide as they move on to more advanced work. 410 0$aChapman Mathematical Notes,$x3005-1517 606 $aAlgebras, Linear 606 $aProbabilities 606 $aMachine learning 606 $aHarmonic analysis 606 $aStatistical physics 606 $aLinear Algebra 606 $aApplied Probability 606 $aMachine Learning 606 $aAbstract Harmonic Analysis 606 $aStatistical Physics 615 0$aAlgebras, Linear. 615 0$aProbabilities. 615 0$aMachine learning. 615 0$aHarmonic analysis. 615 0$aStatistical physics. 615 14$aLinear Algebra. 615 24$aApplied Probability. 615 24$aMachine Learning. 615 24$aAbstract Harmonic Analysis. 615 24$aStatistical Physics. 676 $a515.076 700 $aAlpay$b Daniel$054298 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910857794803321 996 $aExercises in Applied Mathematics$94161301 997 $aUNINA