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Intelligent Systems - UTCN
CS42.00

Description
 | 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.

Crédits ECTS
4

Langue d'enseignement
English

Langue d'examen
English

Langue des supports pédagogiques
English

Acquis d'apprentissage fondamentaux

Entité de gestion (faculté)
Automation and Computer Science Faculty - UTCN