Description

Theory
2

Laboratory
2

Instructors

Isabel Praça

Contents

1 - Types of Problems Where to Use Machine Learning
 

2 - Data and its preprocessing
Data cleaning
Data Integration
Data transformation
Data reduction
 

3- Supervised Learning
Key Concepts
Discriminatory and generative models
Parametric and nonparametric models
 

4- Unsupervised Learning
Clustering
Dimensionality reduction
Kernel methods
 

5 - Regression
Linear Regression
Polynomial Regression
Logistic Regression
 

6 - Decision Trees
Partition Measures
Decision Tree Simplification Techniques
 

7 - Association Rules
Interest Metrics
Rule Generation / Mining
 

8 - Support Vector Machines
Linear and Nonlinear Support Vector Machines
 

9 - Ensemble Methods
Bagging
Boosting

Learning Outcomes

CO1 - Become aware, understand and apply different techniques for data pre-processing steps.
CO2 - Become aware, understand and apply different machine learning techniques: supervised, unsupervised and reinforcement learning.
CO3 - Knowing and understanding a real problem of medium or high complexity and analyzing, investigating, designing, implementing, experimenting, reviewing, testing, synthesizing and evaluating in order to solve this problem using the methods presented in the course
CO4- Develop transversal skills related to teamwork in response to challenges (AI Challenges4Teams)