Fundamentals of Probability and Statistics for Machine Learning

(Hardback)

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Description

An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning. An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning. Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydn offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydn accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data science Emphasizes hands-on learning Covers mathematical fundamentals of probability and statistics and ML basics Suits undergraduates as well as self-learners with basic programming experience Includes slides, solutions, and code

Author Bio

Ethem Alpaydn is Professor in the Department of Computer Engineering at zyegin University and a member of the Science Academy, Istanbul. He is the author of the widely used textbook, Introduction to Machine Learning, now in its fourth edition, and Machine Learning, both published by the MIT Press.