Fundamentals of Probability and Statistics for Machine Learning [Hardcover]

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

Contents
Preface v
1 Introduction 1
1.1 What Is Learning From Data ? 1
1.2 Types of Learning 7
1.3 Relationship to Statistics, Data Science, and Artificial Intelligence 12
1.4 Social, Ethical, and Legal Aspects 13
1.5 Notes 14
1.6 Exercises 15
2 Random Experiments and Probabilities 17
2.1 Random Events 17
2.2 What Is A Probability ? 18
2.3 Equally-Likely Events 19
2.4 Principles of Counting 21
2.5 Some Events May Be More Equal Than Others 24
2.6 The Additive Rule 24
2.7l39