1ª aula

Presentação: Organization, Program, Introduction

What is machine learning: examples, symbolic and statistical learning

1ª aula

Presentação: Organization, Program, Introduction

What is machine learning: examples, symbolic and statistical learning

2ª aula

Perceptron, Backpropagation, RBF network, Kernel trick

3ª aula

The Brain and Information

4ª aula

Knowledge and Learning: EBL, Domain Theory, Analogical Reasoning, Verbal Categories, Visual Categories, Reinforcement Learning

5ª aula

Reinforcement Learning: Passive learning vs. Active Learning, LMS, ADP, TD, Q-Values, Explicit Representation, Genetic Algorithms and Reinforcement Learning

6ª aula

Analogy,Multidimensional Scaling,Structure Mapping Engine,Copycat,PR and AI, Bongard problem, Letter Spirit, Invariant Pattern recognition, Hierarchical Networks, Information and Contours

7ª aula

Cognitive Systems, Soar, Chunking-based Learning, Associative Computing, Learning

8ª aula

The “ideal ra(onal agent” in Ulmatum Game

by  
 
Fernando Pedro Santos

9ª aula

Quantum Probabilisc Graphical Models for Decision and Cognion

by

Catarina Pinto Moreira

10ª aula

ETL

by 

João Pereira 

11ª aula

Autonomic Computing: notions, implementations and challenges

by

Richard Gil Martinez