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 , Cód. 390914.
Lecturer
- Gabriel Pereira Pires (1)(2)
(1) Docente Responsável
(2) Docente que lecciona
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. Descriptive statistics;
3. Simple and multiple linear regression. Nonlinear regression. Parameter estimation through Ordinary Least Squares (OLS) and Gradient Descent (GD). Evaluation of regression models;
4. Regularization methods;
5. Normalization methods and dimensionality reduction;
6. Classifiers: Bayes, Linear Discriminant Analysis, Logistic regression, K-Nearest Neighbors, Decision Trees, Support Vector Machine, Artificial Neural Networks and Convolutional Neural Networks (CNN);
7. Feature extraction methods and feature selection methods;
8. Validation methods and classifier evaluation metrics;
9. 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
- Bishop, C. (2006). Pattern recognition and machine learning. USA: Springer
- Muller, A. e Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. USA: O'Reilly
Teaching Method
Expository classes;
Programming-oriented problem solving classes;
Realization of projects.
Software used in class
Python IDE.
