Publication in the Diário da República: Despacho n.º 13495/2022 - 18/11/2022
10 ECTS; 1º Ano, 1º Semestre, 30,0 PL + 30,0 TP + 30,0 OT
Lecturer
- Gabriel Pereira Pires
- Renato Eduardo Silva Panda
Prerequisites
Algebra and statistics.
Objectives
The main objective of this course is to provide students with knowledge about machine learning with a focus on supervised classification. By the end of this course, it is expected that students will be able to implement all classification steps and apply them to diverse datasets obtained from real problems.
Program
1. Introduction to supervised and unsupervised machine learning;
2. Simple and multiple linear regression. Nonlinear regression. Evaluation of regression models;
3. Methods of normalization and dimensionality reduction;
4. Signal/data characterization, feature extraction methods and feature selection methods;
5. Classifiers: Bayes, Linear Discriminant Analysis, Logistic regression, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks and eventual incursion into some deep learning techniques;
6. Validation methods and classifier evaluation metrics;
7. Application of the methods discussed in different areas (economics, engineering, medicine, etc);
Evaluation Methodology
Assignments (homework): 20%
Individual or group projects: 60%
Assessment test: 20%
Homework and projects have deadlines that are defined throughout the semester.
These evaluation method criteria apply to all evaluation seasons.
Bibliography
- Guido, S. e Muller, A. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. USA: O'Reilly
- Bishop, C. (2006). Pattern recognition and machine learning. USA: Springer
Teaching Method
Expository classes;
Programming-oriented problem solving classes;
Realization of projects.
Software used in class
Python IDE.
