Artificial Intelligence Techniques for Biology


This curricular unit aims to provide the student with skills to:

- The role of ontologies and symbolic reasoning in biology.
- Supervised and unsupervised machine learning fundamentals.
- Elementary notions of neural networks and deep learning.

Be able to:
- Select and correctly apply the methods and models addressed to biology problems
- Critically evaluate the results obtained.
- Use modern machine learning and deep learning libraries.

- Supervised and unsupervised learning algorithms frequently used in biology
- Modern AI tools used in biology.

General characterization





Responsible teacher

João Alexandre Carvalho Pinheiro Leite, Ludwig Krippahl


Weekly - 2

Total - 24

Teaching language






Teaching method

The 28 hours of contact between students and teachers will be divided into 14 hours of lectures and 14 of practical tutorial classes.

The theoretical classes will be supported by class notes provided by the lecturer and references to the appropriate chapters of the recommended textbooks. Each of these classes will be divided into approximately 2/3 exposition and 1/3 free discussion with the students.

The tutorial classes will consist both of exercise classes where students follow sets of exercises provided by the tutors as well as project classes where the students can get help for projects of their conception.

Evaluation method

Lab component: two assignments, each worth 25% of the final grade.


Theoretical component: one test (or exam) worth 50% of the final grade.

Subject matter

1- Introduction to symbolic Artificial Intelligence: knowledge representation by ontologies
2- Semantic Web and applications to biology.
3- Fundamentals of machine learning: supervised and unsupervised; overfitting and model selection.
4- Regression and classification models with artificial neural networks.
5- Introduction to deep learning