Probability in Electrical Engineering and Computer Science: An Application-Driven Course

The book is designed for a junior/senior level course. Applications drive the material: PageRank, Multiplexing, Digital Link, Tracking, Speech Recognition, Route Planning and more. Topics include Markov chains, detection, coding, estimation, Viterbi algorithm, expectation maximization, clustering, compressed sensing, recommender systems, Kalman Filter, Markov decision problems, LQG, and channel capacity. Matlab examples are used to simulate models and to implement the algorithms. Appendices provide the necessary background in basic probability and linear algebra. See https: //sites.google.com/site/walrandpeecs/home.

• Introduction To Probability
• Optimization Models
• ALGORITHMS
• Computer Organization and Design RISC-V Edition: The Hardware Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design)
• Operating Systems: Principles and Practice
• The C Programming Language
• No bullshit guide to linear algebra
• Elements of Programming Interviews in Python: The Insiders' Guide
• Head First Java: A Brain-Friendly Guide
• Deep Learning