TY  - GEN
AU  - Walrand,Jean
TI  - Probability in Electrical Engineering and Computer Science : An Application-Driven Course
SN  - 978-3-030-49995-2
PY  - 2021///
PB  - Springer Nature
KW  - Maths for computer scientists
KW  - bicssc
KW  - Communications engineering / telecommunications
KW  - Maths for engineers
KW  - Probability & statistics
KW  - Probability and Statistics in Computer Science
KW  - Communications Engineering, Networks
KW  - Mathematical and Computational Engineering
KW  - Probability Theory and Stochastic Processes
KW  - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
KW  - Mathematical and Computational Engineering Applications
KW  - Probability Theory
KW  - Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
KW  - Applied probability
KW  - Hypothesis testing
KW  - Detection theory
KW  - Expectation maximization
KW  - Stochastic dynamic programming
KW  - Machine learning
KW  - Stochastic gradient descent
KW  - Deep neural networks
KW  - Matrix completion
KW  - Linear and polynomial regression
KW  - Open Access
KW  - Mathematical & statistical software
KW  - Stochastics
N1  - Open Access
N2  - 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
UR  - https://library.oapen.org/bitstream/20.500.12657/50016/1/978-3-030-49995-2.pdf
UR  - https://directory.doabooks.org/handle/20.500.12854/71297
ER  -