🇵🇹 PT

Objectives

  1. Define the main concepts in the area of Learning Systems and Intelligent Decision Systems, focusing on intelligent problem solving.
  2. Know the various learning paradigms and Intelligent decision making models.
  3. Apply key methods and algorithms to intelligent problem solving.
  4. Use appropriate tools for developing intelligent learning and decision-based problem solving systems.

Program

  1. Introduction: machine learning systems; intelligent decision making models.
  2. Learning paradigms: supervised; unsupervised; reinforced.
  3. Intelligent learning: symbolic and non-symbolic knowledge; learning models and algorithms.
  4. Intelligent decision making: decision making models based on uncertainty; knowledge-based decision making models.
  5. Tools and applications: development tools; application to real problems.
  6. Future trends and conclusions.

Bibliography

  • Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ, USA: Prentice Hall Press.
  • Mitchell, T. M. (1997/2015/2016). Machine Learning (1st ed.). McGraw-Hill International Editions.
  • Hulten, G. (2018). Building Intelligent Systems. Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-3432-7
  • Feinberg, E. A., & Shwartz, A. (2002). Handbook of Markov Decision Processes: Methods and Applications. Springer US.
  • Sutton, R. S., & Barto, A. G. (2015). Reinforcement Learning: An Introduction, (2nd ed.). Cambridge: MIT Press.

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