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Fundamentals of machine learning

Author: Thomas Trappenberg
Publisher: Oxford Oxford University Press 2020
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Interest in machine learning is exploding across the world, both in research and for industrial applications. Fundamentals of Machine Learning provides a brief and accessible introduction to this  Read more...

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Document Type: Book
All Authors / Contributors: Thomas Trappenberg
ISBN: 9780198828044 0198828047
OCLC Number: 1130279328
Description: xi, 247 Seiten : Illustrationen, Diagramme
Contents: 1. Introduction1.1: The basic idea and history of Machine Learning1.2: Mathematical formulation of the basic learning problem1.3: Nonlinear regression in highdimensions1.4: Recent advancements1.5: No free lunchI A PRACTICAL GUIDE TO MACHINE LEARNING2. Scientific programming with Python2.1: Programming environment2.2: Basic language elements2.3: Code efficiency and vectorization2.4: Data handling2.5: Image processing and convolutional filters3. Machine learning with sklearn3.1: Classification with SVC, RFC and MLP3.2: Performance measures and evaluations3.3: Data handling3.4: Dimensionality reduction, feature selection, and tSN3.5: Decision Trees and Random Forests *3.6: Support Vector Machines (SVM) *4. Neural Networks and Keras4.1: Neurons and the threshold perceptron4.2: Multilayer Perceptron (MLP) and Keras4.3: Representational learning4.4: Convolutional Neural Networks (CNNs)4.5: What and Where4.6: More tricks of the tradeII FOUNDATIONS: REGRESSION AND PROBABILISTIC MODELING5. Regression and optimization5.1: Linear regression and gradient descent5.2: Error surface and challenges for gradient descent5.3: Advanced gradient optimization (learning)5.4: Regularization: Ridge regression and LASSO5.5: Nonlinear regression5.6: Backpropagation5.7: Automatic differentiation6. Basic probability theory6.1: Random numbers and their probability (density) function6.2: Moments: mean, variance, etc.6.3: Examples of probability (density) functions6.4: Some advanced concepts6.5: Density functions of multiple random variables6.6: How to combine prior knowledge with new evidence7. Probabilistic regression and Bayes nets7.1: Probabilistic models7.2: Learning in probabilistic models: Maximum likelihood estimate7.3: Probabilistic classification7.4: MAP and Regularization with priors7.5: Bayes Nets: Multivariate causal modeling7.6: Probabilistic and Stochastic Neural Networks8. Generative Models8.1: Modelling classes8.2: Supervised generative models8.3: Naive Bayes8.4: Unsupervised generative models8.5: Generative Neural NetworksIII ADVANCED LEARNING MODELS9. Cyclic Models and Recurrent Neural Networks9.1: Sequence processing9.2: Simple Sequence MLP and RNN in Keras9.3: Gated RNN and attention9.4: Models with symmetric lateral connections10. Reinforcement Learning10.1: Formalization of the problem setting10.2: Modelbased Reinforcement Learning10.3: Modelfree Reinforcement Learning10.4: Deep Reinforcement Learning10.5: Actors and actorcritics11. AI, the brain, and our society11.1: Different levels of modeling and the brain11.2: Machine learning and AI11.3: The impact machine learning technology on society
Responsibility: Thomas P. Trappenberg, Dalhousie University.

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