予測データ解析のための機械学習の基礎（第２版） Fundamentals of Machine Learning for Predictive Data Analytics, second edition : Algorithms, Worked Examples, and Case Studies

著者名：Kelleher, John D./Mac Namee, Brian/D'Arcy, Aoife

The MIT Press（2020/10/20発売）

• 言語：ENG
• ISBN：9780262044691
• eISBN：9780262361101

Description

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Table of Contents

I Introduction to Machine Learning and Data Analytics
1 Machine Learning for Predictive Data Analytics
2 Data to Insights to Decisions
3 Data Exploration
II Predictive Data Analytics
4 Information-Based Learning
5 Similarity-Based Learning
6 Probability-Based Learning
7 Error-Based Learning
8 Deep Learning
9 Evaluation
III Beyond Prediction
10 Beyond Prediction: Unsupervised Learning
11 Beyond Prediction: Reinforcement Learning
IV Case Studies and Conclusions
12 Case Study: Customer Churn
13 Case Study: Galaxy Classification
14 The Art of Machine Learning for Predictive Data Analytics
V Appendices
A Descriptive Statistics and Data Visualization for Machine Learning
B Introduction to Probability for Machine Learning
C Differentiation Techniques for Machine Learning
D Introduction to Linear Algebra
Bibliography
Index