Walrand, Jean <br/><br/>
Probability in Electrical Engineering and Computer Science : An Application-Driven Course 

 -  Springer Nature  2021 
 - 1 electronic resource (380 p.) 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>978-3-030-49995-2 9783030499952 
<br/><br/><label>Standard No.: </label>10.1007/978-3-030-49995-2  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Maths for computer scientists<br/>Communications engineering / telecommunications<br/>Maths for engineers<br/>Probability & statistics
<br/><br/><label>Subjects--Index Terms: </label>Probability and Statistics in Computer Science Communications Engineering, Networks Mathematical and Computational Engineering Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical and Computational Engineering Applications Probability Theory Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 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 Maths for computer scientists Mathematical & statistical software Communications engineering / telecommunications Maths for engineers Probability & statistics Stochastics