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 Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın 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

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 Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın 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

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 Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın 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