ILDM

Intelligent learning and decision making

Objectives

[A] Define the main concepts in the area of Learning Systems and Intelligent Decision Systems, focusing on intelligent problem solving. [B] Know the various learning paradigms and Intelligent decision making models. [C] Apply key methods and algorithms to intelligent problem solving. [D] 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.