Название: Numerical Machine Learning
Автор: 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.