Numerical Machine Learning

By Christopher Teoh, Sayed Ameenuddin Irfan, Zhiyuan Wang

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.

• Format: Paperback
• Language: English
• ISBN: B0CH25SCRL
• ISBN13: 9789815165005
• Release Date: August 2023
• Publisher: Bentham Science Publishers
• Length: 226 Pages
• Weight: 1.21 lbs.
• Dimensions: 0.6" x 7.0" x 10.0"