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008			190718s2019 flu ob 001 0 eng
010			_a 2019030251
020			_z9781138492530
040			_aDLC _beng _cDLC _erda _dEG-NcFUE
042			_apcc
050	0	0	_aQ325.5
082	0	0	_a006.31 _223 _bK.D.D
100	1		_aKroese, Dirk P., _eauthor.
245	1	0	_aData science and machine learning : _bmathematical and statistical methods / _cDirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman.
250			_aFirst edition.
263			_a1912
264		1	_aBoca Raton : _bCRC Press, _c2019.
264		4	_c©2020
300			_axxi, 513 pages : _bcolor charts, color illustrations; _c30 cm
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
490	0		_aChapman & Hall/CRC machine learning & pattern recognition
504			_aIncludes bibliographical references and index.
505			_aPreface Notation Importing, Summarizing, and Visualizing Data Introduction Structuring Features According to Type Summary Tables Summary Statistics Visualizing Data Plotting Qualitative Variables Plotting Quantitative Variables Data Visualization in a Bivariate Setting Exercises Statistical Learning Introduction Supervised and Unsupervised Learning Training and Test Loss Tradeoffs in Statistical Learning Estimating Risk In-Sample Risk Cross-Validation Modeling Data Multivariate Normal Models Normal Linear Models Bayesian Learning Exercises Monte Carlo Methods Introduction .Monte Carlo Sampling Generating Random Numbers Simulating Random Variables Simulating Random Vectors and Processes Resampling Markov Chain Monte Carlo Monte Carlo Estimation Crude Monte Carlo Bootstrap Method Variance Reduction Monte Carlo for Optimization Simulated Annealing Cross-Entropy Method Splitting for Optimization Noisy Optimization Exercises Unsupervised Learning Introduction Risk and Loss in Unsupervised Learning Expectation-Maximization (EM) Algorithm Empirical Distribution and Density Estimation Clustering via Mixture Models Mixture Models EM Algorithm for Mixture Models Clustering via Vector Quantization K-Means Clustering via Continuous Multiextremal Optimization Hierarchical Clustering Principal Component Analysis (PCA) Motivation: Principal Axes of an Ellipsoid PCA and Singular Value Decomposition (SVD) Exercises Regression Introduction Linear Regression Analysis via Linear Models Parameter Estimation Model Selection and Prediction Cross-Validation and Predictive Residual Sum of Squares In-Sample Risk and Akaike Information Criterion Categorical Features Nested Models Coefficient of Determination Inference for Normal Linear Models Comparing Two Normal Linear Models Confidence and Prediction Intervals Nonlinear Regression Models Linear Models in Python Modeling Analysis Analysis of Variance (ANOVA) Confidence and Prediction Intervals Model Validation Variable Selection Generalized Linear Models Exercises Regularization and Kernel Methods Introduction Regularization Reproducing Kernel Hilbert Spaces Construction of Reproducing Kernels Reproducing Kernels via Feature Mapping Kernels from Characteristic Functions Reproducing Kernels Using Orthonormal Features Kernels from Kernels Representer Theorem Smoothing Cubic Splines Gaussian Process Regression Kernel PCA Exercises Classification Introduction Classification Metrics Classification via Bayes' Rule Linear and Quadratic Discriminant Analysis Logistic Regression and Softmax Classification K-nearest Neighbors Classification Support Vector Machine Classification with Scikit-Learn Exercises Decision Trees and Ensemble Methods Introduction Top-Down Construction of Decision Trees Regional Prediction Functions Splitting Rules Termination Criterion Basic Implementation Additional Considerations Binary Versus Non-Binary Trees Data Preprocessing Alternative Splitting Rules Categorical Variables Missing Values Controlling the Tree Shape Cost-Complexity Pruning Advantages and Limitations of Decision Trees Bootstrap Aggregation Random Forests Boosting Exercises Deep Learning Introduction Feed-Forward Neural Networks Back-Propagation Methods for Training Steepest Descent Levenberg-Marquardt Method Limited-Memory BFGS Method Adaptive Gradient Methods Examples in Python Simple Polynomial Regression Image Classification Exercises Linear Algebra and Functional Analysis Vector Spaces, Bases, and Matrices Inner Product Complex Vectors and Matrices Orthogonal Projections Eigenvalues and Eigenvectors Left- and Right-Eigenvectors Matrix Decompositions (P)LU Decomposition Woodbury Identity Cholesky Decomposition QR Decomposition and the Gram-Schmidt Procedure Singular Value Decomposition Solving Structured Matrix Equations Functional Analysis Fourier Transforms Discrete Fourier Transform Fast Fourier Transform Multivariate Differentiation and Optimization Multivariate Differentiation Taylor Expansion Chain Rule Optimization Theory Convexity and Optimization Lagrangian Method Duality Numerical Root-Finding and Minimization Newton-Like Methods Quasi-Newton Methods Normal Approximation Method Nonlinear Least Squares Constrained Minimization via Penalty Functions Probability and Statistics Random Experiments and Probability Spaces Random Variables and Probability Distributions Expectation Joint Distributions Conditioning and Independence Conditional Probability Independence Expectation and Covariance Conditional Density and Conditional Expectation Functions of Random Variables Multivariate Normal Distribution Convergence of Random Variables Law of Large Numbers and Central Limit Theorem Markov Chains Statistics Estimation Method of Moments Maximum Likelihood Method Confidence Intervals Hypothesis Testing Python Primer Getting Started Python Objects Types and Operators Functions and Methods Modules Flow Control Iteration Classes Files NumPy Creating and Shaping Arrays Slicing Array Operations Random Numbers Matplotlib Creating a Basic Plot Pandas Series and Data Frame Manipulating Data Frames Extracting Information Plotting Scikit-learn Partitioning the Data Standardization Fitting and Prediction Testing the Model System Calls, URL Access, and Speed-Up Bibliography Index
520			_a"The purpose of this book is to provide an accessible, yet comprehensive, account of data science and machine learning. It is intended for anyone interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science"-- _cprovided by publisher.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
650		0	_aMachine learning _xMathematics.
650		0	_aMachine learning _xStatistical methods.
650		0	_aMathematical analysis.
700	1		_aBotev, Zdravko I., _d1982- _eauthor.
700	1		_aTaimre, Thomas, _d1983- _eauthor.
700	1		_aVaisman, Radislav, _eauthor.
776	0	8	_iPrint version: _aKroese, Dirk P.. _tMathematical and statistical methods for data science and machine learning _bFirst edition. _dBoca Raton : CRC Press, 2019. _z9781138492530 _w(DLC) 2019030250
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK