Probability in Electrical Engineering and Computer Science
An Application-Driven Course
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Beschreibung
This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. The companion website now has many examples of Python demos and also Python labs used in Berkeley.
- Showcases techniques of applied probability with applications in EE and CS;
- Presents all topics with concrete applications so students see the relevance of the theory;
- Illustrates methods with Jupyter notebooks that use widgets to enable the users to modify parameters.
This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book.
Showcases techniques of applied probability with applications in EE and CS Presents all topics with concrete applications so students see the relevance of the theory Illustrates methods with Jupyter notebooks that use widgets to enable the users to modify parameters This book is open access, which means that you have free and unlimited access
Autor*in
Jean Walrand
Themen in »Probability in Electrical Engineering and Computer Science«
Applied probability Hypothesis testing Detection theory Expectation maximization Stochastic dynamic programming Machine learning Stochastic gradient descent Deep neural networks Matrix completion Linear and polynomial regression Open Access