Decision Support Systems

Code

200121

Academic unit

NOVA Information Management School

Credits

7.5

Teacher in charge

Mauro Castelli

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Objectives

Present algorithms that can be used for extracting knowledge from datasets. In particular, the course will cover the case of supervised learning.

Prerequisites

No

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 

Bibliography

Machine Learning. Tom Mitchell ; Papers and materials provided by the professor; 0; 0; 0

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. 

Courses

• PostGraduate Information Systems and Technologies Management
• PostGraduate in Knowledge Management and Business Intelligence
• PostGraduate in Information Systems Governance
• PostGraduate in Marketing Research e CRM
• PostGraduate in Intelligence Management and Security
• PostGraduate in Marketing Intelligence
• PostGraduate in Enterprise Information Systems
• Mobilidade Universitária
• PostGraduate in Digital Enterprise Management
• PostGraduate Marketing Research e CRM
• PostGraduate in Information Systems and Technologies Management
• PostGraduate Risk Analysis and Management
• PostGraduate in Smart Cities
• PostGraduate in Information Management and Business Intelligence in Healthcare
• PostGraduate Digital Marketing and Analytics
• PostGraduate in Information Analysis and Management