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Fundamentals of Probability and Statistics for Machine Learning
Hardback

Fundamentals of Probability and Statistics for Machine Learning

$199.99
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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 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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MORE INFO
Format
Hardback
Publisher
MIT Press Ltd
Country
United States
Date
16 December 2025
Pages
544
ISBN
9780262049818

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 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

Read More
Format
Hardback
Publisher
MIT Press Ltd
Country
United States
Date
16 December 2025
Pages
544
ISBN
9780262049818