Machine Learning in Biomedical Engineering
In the end of this curricular unit, the student should:
- Know the history of Machine learning and the inspiration connection points to the biological systems.
- Be able to understand the global functioning mechanisms of the most relevant machine learning algorithms.
- Distinguish between supervised and non-supervised machine learning.
- Know statistical and neuronal methods for solving machine learning problems.
- Be able to model a problem in the context of machine learning methods
- Be able to use machine learning tools to solve biomedical engineering problems
Be able to report and interpret the evaluation of a machine learning system
Hugo Filipe Silveira Gamboa
Weekly - 3
Total - 42
Base knowledge on linear algebra, statistics and programming.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
by Aurélien Géron, O’Reilly, 2019
Pattern Classification, 2nd Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley, 2000
Deep Learning, by Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press 2016,
Teoretical classes will present the base context with exercicie examples.A aulas teóricas
Practical lectures will be bases on programing guides to be solved by the students.
Exercisces 10%; Tests 40%; Project 50%
1. Overview of the current state of Machine Learning.
2. Base concepts and connection to biological principles.
3. Supervised learning. Classification and regression problems.
4. Unsupervised learning. Clustering and exploration.
5. Deep neural networks techniques.
6. Other approaches to machine learning.
7. Example applications on biomedical engineering
Programs where the course is taught: