LEADER 03445nam 22005895 450 001 9910338008103321 005 20200701190132.0 010 $a9781523150373 010 $a1523150378 010 $a9781484239162 010 $a1484239164 024 7 $a10.1007/978-1-4842-3916-2 035 $a(CKB)4100000007591523 035 $a(MiAaPQ)EBC5660308 035 $a(DE-He213)978-1-4842-3916-2 035 $a(CaSebORM)9781484239162 035 $a(PPN)233802363 035 $a(OCoLC)1104211878 035 $a(OCoLC)on1104211878 035 $a(EXLCZ)994100000007591523 100 $a20190131d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMATLAB Machine Learning Recipes $eA Problem-Solution Approach /$fby Michael Paluszek, Stephanie Thomas 205 $a2nd ed. 2019. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019. 215 $a1 online resource (358 pages) 311 08$a9781484239155 311 08$a1484239156 320 $aIncludes bibliographical references. 327 $a1 Overview -- 2 Data Representation -- 3 MATLAB Graphics -- 4 Kalman Filters -- 5 Adaptive Control -- 6 Fuzzy Logic -- 7 Data Classification with Decision Trees -- 8 Simple Neural Nets -- 9 Classification with Neural Nets -- 10 Neural Nets with Deep Learning -- 11 Neural Aircraft Control -- 12 Multiple Hypothesis Testing -- 13 Autonomous Driving with MHT -- 14 Case-Based Expert Systems -- Appendix A: A Brief History of Autonomous Learning -- Appendix B: Software for Machine Learning. 330 $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 . 606 $aArtificial intelligence 606 $aBig data 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aArtificial intelligence. 615 0$aBig data. 615 14$aArtificial Intelligence. 615 24$aBig Data. 676 $a006.3 700 $aPaluszek$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut$0887778 702 $aThomas$b Stephanie$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910338008103321 996 $aMATLAB Machine Learning Recipes$92510917 997 $aUNINA