Describes the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.
Describes the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Max A. Little is Professor of Mathematics at Aston University, UK, and a world-leading expert in signal processing and machine learning. His research in machine learning for digital health is highly influential and is the basis of advances in basic and applied research into quantifying neurological disorders such as Parkinson disease. He has published over 60 articles in the scientific literature on the topic, two patents, and a textbook. He is an advisor to government and leading international corporations in topics such as machine learning for health.
Inhaltsangabe
1: Mathematical Foundations 2: Optimization 3: Random Sampling 4: Statistical Modelling and Inference 5: Probabalistic Graphical Models 6: Statistical Machine Learning 7: Linear-Gaussian Systems and Signal Processing 8: Discrete Signals: Sampling, Quantization and Coding 9: Nonlinear and Non-Gaussian Signal Processing 10: Nonparametric Bayesian Machine Learning and Signal Processing
1: Mathematical Foundations 2: Optimization 3: Random Sampling 4: Statistical Modelling and Inference 5: Probabalistic Graphical Models 6: Statistical Machine Learning 7: Linear-Gaussian Systems and Signal Processing 8: Discrete Signals: Sampling, Quantization and Coding 9: Nonlinear and Non-Gaussian Signal Processing 10: Nonparametric Bayesian Machine Learning and Signal Processing
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