Decision Support Systems
Objectives
Present algorithms that can be used for extracting knowledge from datasets. In particular, the course will cover the case of supervised learning.
General characterization
Code
200121
Credits
7.5
Responsible teacher
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
No
Bibliography
Machine Learning, Tom Mitchell, McGraw Hill, 1997.
Papers and materials provided by the professor.
Teaching method
Theoretical classes where the different techniques commonly used for taking profitable decisions are presented
Evaluation method
First epoch: two tests. The final grade is he average of the two tests. A minimum grade is required in both the two tests.
Second epoch: final written exam.
Subject matter
Optimization problems: definitions and examples.
No free lunch theorem
Local search techniques
Population-based optimization algorithms
Bio-inspired machine learning techniques
Supervised and unsupervised learning
Multi criteria optimization and Pareto dominance
Neural Networks
Programs
Programs where the course is taught: