1ª aula

presentação: Organization, Program, Introduction

What is machine learning: examples

1ª aula

presentação: Organization, Program, Introduction

What is machine learning: examples

2ª aula

What is supervised statistical machine learning? Peceptron, Back-propagation algorithm, Kernel-machines

3ª aula

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



4ª aula

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

5ª aula

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

Brain and Information

7ª aula

Cased-Based Reasoning and Games (by  Samuel Mascarenhas)

8ª aula

Thermodynamics, Entropy, Macroscoping and Microscoping state, Dice model , Information and Entropy, Definition of Entropy as the number of Questions, Communication, Message, Information Theory, Representation of information, Signal alphabet, Surprise, Information and probability, Statistical encoding

9ª aula

BIOLOGICALLY INSPIRED BIOLOGICALLY INSPIRED BIOLOGICALLY INSPIRED BIOLOGICALLY INSPIRED COMPUTER MODELS COMPUTER MODELS COMPUTER MODELS  FOR VISUAL RECOGNITIONFOR VISUAL RECOGNITIONFOR VISUAL RECOGNITIONFOR VISUAL RECOGNITION  (by  Ângelo Cardoso)

Virtual agents and learning (by Pedro Sequeira)


10ª aula

Brain and Memory (by  João Sacramento)

Recurent Networks (by J ose Matos)

11ª aula

Cognitive Models and Interfaces (by  Andreia Artífice)