LEADER 01218nam 2200361 450 001 9910211252203321 005 20230809224917.0 010 $a1-5386-2795-7 035 $a(CKB)3710000001416570 035 $a(WaSeSS)IndRDA00103219 035 $a(EXLCZ)993710000001416570 100 $a20180815d2017 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSECM 2017 $e2017 IEEE/ACM 1st International Workshop on Software Engineering Curricula for Millennials : proceedings : 27 May 2017, Buenos Aires, Argentina /$feditors, Hakan Erdogmus, Ce?cile Pe?raire ; sponsored by SIGSOFT 210 1$aPiscataway, New Jersey :$cIEEE Press,$d2017. 215 $a1 online resource (86 pages) 311 $a1-5386-2796-5 606 $aSoftware engineering$xStudy and teaching$vCongresses 615 0$aSoftware engineering$xStudy and teaching 676 $a005.1071 702 $aErdogmus$b Hakan 702 $aPe?raire$b Ce?cile 712 02$aACM Sigsoft, 801 0$bWaSeSS 801 1$bWaSeSS 906 $aPROCEEDING 912 $a9910211252203321 996 $aSECM 2017$91964411 997 $aUNINA LEADER 06093nam 22006735 450 001 9910337617403321 005 20200702145418.0 010 $a3-030-18545-1 024 7 $a10.1007/978-3-030-18545-9 035 $a(CKB)4100000008527471 035 $a(DE-He213)978-3-030-18545-9 035 $a(MiAaPQ)EBC5923635 035 $a(PPN)242849539 035 $a(EXLCZ)994100000008527471 100 $a20190629d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPython for Probability, Statistics, and Machine Learning /$fby José Unpingco 205 $a2nd ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XIV, 384 p. 164 illus., 37 illus. in color.) 311 $a3-030-18544-3 327 $aIntroduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index. 330 $aThis book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. 606 $aElectrical engineering 606 $aMathematical statistics 606 $aApplied mathematics 606 $aEngineering mathematics 606 $aStatistics 606 $aData mining 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aElectrical engineering. 615 0$aMathematical statistics. 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 0$aStatistics. 615 0$aData mining. 615 14$aCommunications Engineering, Networks. 615 24$aProbability and Statistics in Computer Science. 615 24$aMathematical and Computational Engineering. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aData Mining and Knowledge Discovery. 676 $a005.133 676 $a005.133 700 $aUnpingco$b José$4aut$4http://id.loc.gov/vocabulary/relators/aut$0762830 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910337617403321 996 $aPython for Probability, Statistics, and Machine Learning$91547068 997 $aUNINA