Principles and Theory for Data Mining and Machine Learning by Springer

Data mining and machine learning are two of the most important and rapidly growing fields in computer science today. They are used in a wide variety of applications, from fraud detection and medical diagnosis to customer segmentation and predictive analytics.

Principles and Theory for Data Mining and Machine Learning by Springer is an essential resource for anyone who wants to understand the core concepts of these fields. The book provides a comprehensive overview of the fundamental algorithms and techniques used in data mining and machine learning, as well as a deep dive into the theoretical foundations of these methods.

• Comprehensive coverage of the fundamental algorithms and techniques used in data mining and machine learning
• Deep dive into the theoretical foundations of these methods
• Clear and concise explanations
• Extensive use of examples and illustrations
• Exercises and study questions at the end of each chapter

Principles and Theory for Data Mining and Machine Learning is intended for students, researchers, and practitioners in the fields of data mining, machine learning, and computer science. The book is also a valuable resource for anyone who wants to learn more about these important topics.

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)

by John R. Erickson

5 out of 5

Language	:	English
File size	:	19169 KB
Screen Reader	:	Supported
Print length	:	798 pages
Paperback	:	41 pages
Item Weight	:	5.8 ounces
Dimensions	:	8.5 x 0.1 x 11 inches
X-Ray for textbooks	:	Enabled

FREE

Yingying Chen is a professor in the Department of Computer Science at the University of California, Berkeley. She is a leading researcher in the fields of data mining and machine learning, and she has published over 100 papers in these areas.

• Data Mining
  • Data Preprocessing
  • Data Transformation
  • Data Reduction
  • Data Mining Algorithms
• Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• Theoretical Foundations
  • Probability and Statistics
  • Linear Algebra
  • Optimization
• Applications
  • Fraud Detection
  • Medical Diagnosis
  • Customer Segmentation
  • Predictive Analytics

"This book is a comprehensive and well-written to the principles and theory of data mining and machine learning. It is a valuable resource for students, researchers, and practitioners in these fields." - Professor Jiawei Han, University of Illinois at Urbana-Champaign

"This book provides a clear and concise overview of the fundamental algorithms and techniques used in data mining and machine learning. It is a valuable resource for anyone who wants to learn more about these important topics." - Dr. Pang-Ning Tan, Microsoft Research

Principles and Theory for Data Mining and Machine Learning by Springer is an essential resource for anyone who wants to understand the core concepts of these fields. The book provides a comprehensive overview of the fundamental algorithms and techniques used in data mining and machine learning, as well as a deep dive into the theoretical foundations of these methods.

If you are interested in learning more about data mining and machine learning, I highly recommend this book.

Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics)

by John R. Erickson

5 out of 5

Language	:	English
File size	:	19169 KB
Screen Reader	:	Supported
Print length	:	798 pages
Paperback	:	41 pages
Item Weight	:	5.8 ounces
Dimensions	:	8.5 x 0.1 x 11 inches
X-Ray for textbooks	:	Enabled

FREE