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 Alpaydin offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydin 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
- Covers mathematical fundamentals of probability and statistics and ML basics
- Emphasizes hands-on learning
- Suits undergraduates as well as self-learners with basic programming experience
- Includes slides, solutions, and code
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ISBN-13
978-0-262-38381-3 (9780262383813)
Schweitzer Klassifikation
Ethem Alpaydin is Professor in the Department of Computer Engineering at Özyegin 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.
