Machine Learning for Biomedical Applications

With Scikit-Learn and PyTorch

Maria Deprez,Emma C. Robinson

• Éditeur: Academic Press
• Année: 2023
• ISBN:
  9780128229057
• Sujet: Ciencia de la computación
• Sous-sujet: Visión y reconocimiento de patrones computacionales

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.

• Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis.
• Shows how to apply a range of commonly used machine learning and deep learning techniques to biomedical problems.
• Develops practical computational skills needed to implement machine learning and deep learning models for biomedical data sets.
• Shows how to design machine learning experiments that address specific problems related to biomedical data

Table des matières

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• Chapter 1: Programming in Python
• Chapter 2: Machine learning basics
• Chapter 3: Regression
• Chapter 4: Classification
• Chapter 5: Dimensionality reduction
• Chapter 6: Clustering
• Chapter 7: Decision trees and ensemble learning
• Chapter 8: Feature extraction and selection
• Chapter 9: Deep learning basics
• Chapter 10: Fully connected neural networks
• Chapter 11: Convolutional neural networks
• References
• Index