Description

Theory
2

Theory/Practice
1

Laboratory
2

Instructors

Elsa Ferreira Gomes

Contents

CP1. Introduction to Machine Learning. (5%)
CP2. Supervised learning and non-supervised. (5%)
CP3. Linear Regression. Multiple Linear Regression. (10%)
CP4. Classification: Logistic Regression and Linear Discriminant Analysis (LDA). (10%)
CP5. Resampling methods: Cross validation and the Bootstrap. (10%)
CP6. Regularization methods: Ridge regression and LASSO. (10%)
CP7. Non-linear models: Splines. Generalized Additive Models (GAM). (10%)
CP8. Tree based methods: Decision trees and Random Forests. (10%)
CP9. Support vector machines (SVM). (10%)
CP10. Unsupervised machine learning techniques. Principal Component Analysis (PCA). Clustering algorithms: k-means and Hierarchical clustering. (10%)
CP11. Introduction to Reinforcement Learning. (10%)

Learning Outcomes

By the end of this course the student should be able to:
CO1. Explain the main machine learning methods and algorithms (BL:2/6).
CO2. Select and apply technics, methods, and concepts of machine learning (BL:4/6).
CO3. Implement machine learning processes and analyse the results obtained (BL: 4/6).
CO4. Evaluate, compare, and judge different models (BL:5/6) .