Treatment of random variables and expectations focused on discrete cases for relevant computer science applications
Practical simulation techniques and emphasis on Markov chains to derive probabilities and expectations
Clear accounts of point inference strategies, maximum likelihood, Bayesian inference, and confidence intervals
Chapters on classification, regression, and clustering with real-world examples and programming exercises
Instructor resources including model solutions and presentation slides for enhanced teaching
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This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. With careful treat