LEADER 02959cam a2200301 i 4500
001 991004034169707536
008 211207t2019    nyua     b    001 0 eng d
020   $a9781484239155
035   $ab14427096-39ule_inst
040   $aBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. Ingegneria Innovazione$beng
082 04$a006.31$223
100 1 $aPaluszek, Michael$0887778
245 10$aMATLAB machine learning recipes :$ba problem-solution approach /$cMichael Paluszek and Stephanie Thomas
250   $a2nd ed.
264  1$aNew York :$bApress,$cc2019
300   $axix, 347 p. :$bill. ;$c26 cm
504   $aIncludes bibliographical references and index
505 0 $aIntroduction -- An overview of machine learning -- Representation of data for machine learning in MATLAB -- MATLAB graphics -- Kalman filters -- Adaptive control -- Fuzzy logic -- Data classification with decision trees -- Introduction to neural nets -- Classification of numbers using neural networks -- Pattern recognition with deep learning -- Neural aircraft control -- Multiple hypothesis testing -- Autonomous driving with multiple hypothesis testing -- Case-based expert systems -- A brief history of autonomous learning -- Software for machine learning
520   $aHarness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. You will: Learn to write code for machine learning, adaptive control and estimation using MATLAB See how these three areas complement each other Understand why these three areas are needed for robust machine learning applications Use MATLAB graphics and visualization tools for machine learning Code real world examples in MATLAB for major applications of machine learning in big data
650  0$aMachine learning
700 1 $aThomas, Stephanie$eauthor$4http://id.loc.gov/vocabulary/relators/aut$01065669
907   $a.b14427096$b10-12-21$c07-12-21
912   $a991004034169707536
945   $aLE026 006.31 D PAL 01.01 C.1 2019$cC.1$g1$i2026000134628$lle026$nProf. Lay-Ekuakille / Biblioteca$op$pE26.74$q-$rn$s-  $t1$u0$v0$w0$x0$y.i15989239$z09-12-21
945   $aLE026 006.31 D PAL 01.01 C.2 2019$cC.2$g1$i2026000134635$lle026$nProf. Lay-Ekuakille / Biblioteca$op$pE26.74$q-$rl$s-  $t0$u0$v0$w0$x0$y.i15989744$z10-12-21
996   $aMATLAB machine learning recipes$92547442
997   $aUNISALENTO
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