| Introduction.
| Learning from Examples. Learning decision trees.
| Hypothesis evaluation. Overfitting. Regression and classifcation. Naive Bayes classifier.
| Non-parametric learning. Support Vector Machines. K-Nearest Neighbor. Ensemble Learning.
| Artificial Neural Networks.
| Deep Learning: convolutional neural networks (CNN), recurrent Neural networks (RNN). Regularization.
| Transformers. Attention Mechanism. Language Models. Natural Language Processing with Deep Learning. Information Retrieval. Word-to-vector representation.
| Unsupervised learning. Association mining: frequent set generation, rule generation, compact representation of frequent sets
| Unsupervised learning. Data clustering algorithms. K-means. Hierarchical clustering.
| Making complex decisions: value iteration, policy iteration, partially observable MDP, game theory.
| Reinforcement Learning
| Neuro-symbolic integration. Knowledge in Learning: explanation-based learning, relevant information, Inductive Logic Programming
| BDI Agents: goals, events, plan selection, values.
| Explainable AI. Ethics and responsability.
