Numerical Machine Learning

Zhiyuan Wang, Sayed Ameenuddin Irfan, Christopher Teoh

• Publisher: Bentham Science Publishers
• Year: 2000
• Print ISBN: 9789815165005
• eBook ISBN: 9789815136982
• Topic: Informatik
• Subtopic: Datenverarbeitung

Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.

Table of contents

• Welcome
• Table of Content
• Title
• BENTHAM SCIENCE PUBLISHERS LTD.
• PREFACE
• Introduction to Machine Learning
• Linear Regression
• Regularization
• Logistic Regression
• Decision Tree
• Gradient Boosting
• Support Vector Machine
• K-means Clustering