Machine Learning for Biomedical Applications
With Scikit-Learn and PyTorch

An easy to understand, interactive learning resources that presents machine and deep learning methods as applied to biomedical engineering

Maria Deprez (Author), Emma C. Robinson (Author)

9780128229040, Elsevier Science

Paperback / softback, published 13 September 2023

304 pages, 84 illustrations (48 in full color)
23.5 x 19 x 2 cm, 1 kg

Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more.

This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.

1. Programming in Python
2. Machine Learning Basics
3. Regression
4. Classification
5. Dimensionality reduction
6. Clustering
7. Ensemble methods
8. Feature extraction and selection
9. Introduction to Deep Learning
10. Neural Networks
11. Convolutional Neural Networks

Subject Areas: Computer vision [UYQV], Neural networks & fuzzy systems [UYQN], Machine learning [UYQM], Artificial intelligence [UYQ]