Название: Numerical Machine LearningАвтор: Zhiyuan Wang, Sayed Ameenuddin Irfan, Christopher Teoh
Издательство: Bentham Books
Год: 2023
Страниц: 225
Язык: английский
Формат: pdf (true), epub
Размер: 32.3 MB
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.
From our experiences of teaching Machine Learning using various textbooks, we have noticed that there tends to be a strong emphasis on abstract mathematics when discussing the theories of Machine Learning algorithms. On the other hand, in the application of Machine Learning, it usually straightaway goes to import offthe- shelf libraries such as scikit-learn, TensorFlow, Keras, and PyTorch. The disconnect between abstract mathematical theories and practical application creates a gap in understanding. This book bridges the gap using numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of Machine Learning algorithms. By working through concrete examples step by step, readers/students can develop a well-rounded understanding of these algorithms, gain a more indepth knowledge of how mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems.
Beginning with an introduction to Machine Learning in Chapter 1, the remaining chapters of the book cover seven commonly used Machine Learning algorithms and techniques, including both supervised and unsupervised learning, as well as both linear and nonlinear models. The book requires some prerequisite knowledge of basic probability and statistics, linear algebra, calculus, and Python programming. The book is intended for university students studying Machine Learning and is used as our primary teaching material for the “Introduction to Machine Learning” module at DigiPen Institute of Technology Singapore.
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.
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