Knowledge Discovery end Decision Support Systems

Learning Outcomes

Upon successful completion of the course, the student will be able:

• to understand the complex and iterative process of knowledge discovery from data,
• to list the types of knowledge that are generated and the algorithms that perform the mining,
• to understand and apply machine learning algorithms to data,
• to perceive under/over-fitting phenomena and deal with them,
• to apply the above in the context of decision support.

Indicative Module Content

• Knowledge discovery from data – the steps.
• Predictive Models and Informative Patterns as Tools for Decision Making.
• Knowledge discovery as an application of machine learning algorithms.
• Supervised learning:
  • classification,
  • regression.
• Unsupervised learning
  • clustering,
  • association rules.
• Neural Networks.
• Deep Learning, Deep Neural Networks.
• Evaluation metrics.
• Problems in Knowledge Discovery.

Module’s Address in eClass

• https://eclass.uth.gr/courses/DS_P_121/