Teaching Methodologies
In theoretical classes, an expository methodology will be used for the presentation of the various contents, and, in continuity, in practical-laboratory classes these same contents will be computationally experimented, by solving exercises that will include problems with biomedical application.
The knowledge assessment will comprise two components: the theoretical consisting of an exam scored for 14 points, and the practice consisting of a work scored for 6 points, which will consist of an IDA project, involving a simple biomedical application. In any of the components, a minimum classification of 35% will be required.
Learning Results
This curricular unit aims to provide students with the basics of intelligent data analysis (IDA), based on machine learning (ML)algorithms. The objectives are therefore: 1. Know to identify the phases of an IDA project; 2. Know the methods for data pre-processing, as well as the main techniques for data transformation; 3. Know the theoretical foundations of supervised and unsupervised ML algorithms, and know in what situation to apply them; 4. Be able to experimentally validate the various algorithms and evaluate them through the interpretation of performance metrics; 5. Undertake a complete IDA project involving a simple biomedical application.
Program
1. Introduction to intelligent data analysis
2. Data pre-processing
3. Missing data fill, noise reduction, outliers
4. Data transformation
5. Normalization, dimensionality reduction, feature selection
6. Supervised learning models: classification and regression
7. Models of unsupervised learning
8. Performance metrics
9. Biomedical applications
Internship(s)
NAO
Bibliography
– Rebala G., Ravi A., & Churiwala S. (2019). An Introduction to Machine Learning. Springer.
– Kubat M. (2021). An Introduction to Machine Learning. Springer.
– Larose, D. T., & Larose, C. D. (2nd Ed.). (2014), Discovering Knowledge in Data: An Introduction to Data Mining. Wiley.
– Han J., Kamber M., & Pei J. (3rd Ed.). (2012). Data Mining Concepts and Techniques. Elsevier.
– Müller, A. C., & Guido S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly.