This course will help a student acquire knowledge, skills and insight in machine learning in the domain of medical imaging and sensor data. The course will begin with an introduction to the classical techniques in classification, supervised and unsupervised learning, and regression. This will be followed by the newly developed field of machine learning. While the concepts are general, the focus will be on the medical applications of these concepts.
The course addresses the machine learning and deep learning paradigm for classification and pattern recognition in vast amounts of data. This course is an introduction to classical machine learning and the new paradigms such as convolutional neural nets for deep learning. The topics that will be covered include:
- Architectures for classification and regression
- Overfitting, underfitting, and the generalization gap
- Strategies for learning such as convolutional neural networks
- Hardware and software platforms for machine learning
- The medical context and significance of machine learning
PhD students, young scientists and UT students with a background in Biomedical Technology, Technical Medicine, and related fields. Primarily for students with a need for more insight in machine learning with application to medical image data.
7 - 12 June 2021
5 full days