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, 2nd Edition
• Algorithms
• Optimization Models
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Operating Systems: Principles and Practice
• Convex Optimization
• C Programming Language, 2nd Edition
• Linear Algebra Done Right (Undergraduate Texts in Mathematics)
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems