Machine Learning: A First Course For Engineers And Scientists Thomas B Schön Cambridge University Press Αγγλικά Βιβλία Computing Technology

This Book Introduces Machine Learning For Readers With Some Background In Basic Linear Algebra Statistics Probability And Programming A Coherent Statistical Framework It Covers Selection Of Supervised Methods From The Most Fundamental (K-Nn Decision Trees Logistic Regression) To More Advanced (Deep Neural Networks Support Vector Machines Gaussian Processes Random Forests Boosting) Plus Commonly-Used Unsupervised (Generative Modeling K-Means Pca Autoencoders Generative Adversarial Networks) Careful Explanations Pseudo-Code Are Presented All Authors Maintain Focus On Fundamentals By Drawing Connections Between Discussing General Concepts Such As Loss Functions Maximum Likelihood Bias-Variance Decomposition Ensemble Averaging Kernels Bayesian Approach Along Generally Useful Tools Regularization Cross Validation Evaluation Metrics Optimization Final Chapters Offer Practical Advice Solving Real-World Problems Ethical Aspects Modern Σελίδες: 350 Διαστάσεις: 18 2X18 2Cm