Machine Learning

The material is presented in theoretical-practical classes that include the exposition of theory and the demonstration of its application with
practical examples. It is intended that these examples help in the elaboration of a project that will be built throughout the semester in order
to allow having a global perspective of what a solution in the area of Machine Learning is.

This curricular unit aims to provide the student with a set of machine learning knowledge that allows him to develop solutions to problems
involving data analysis and decision making. Specifically, it is intended that the student knows the basics of machine learning, masters the
most common techniques, knows how to identify which techniques are most appropriate for a given problem, knows how to evaluate
models and how to fine-tune them. It is intended that in the end the student will be able to use the acquired knowledge in carrying out a
practical project representing a real problem.

1 – Introduction to Machine Learning
2 – Traditional supervised techniques
3 – Traditional unsupervised techniques
4 – Dimensionality Reduction
5 – Model evaluation and tunning
6 – Anomaly Detection and Diagnosis
7 – Introduction to Deep Learning
8 – Edge artificial intelligence (AI)
9 – Recommender Systems

NAO