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

General characterization





Responsible teacher

Hugo Filipe Silveira Gamboa


Weekly - 3

Total - 42

Teaching language



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,




Teaching method

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. 

Evaluation method

Exercisces 10%; Tests 40%; Project 50%

Subject matter

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: