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This course is an introduction to computational theories of human cognition.
0
English
English [CC]
FREE
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Description
Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?
Course content
- Introduction Unlimited
- Foundations of Inductive Learning Unlimited
- Knowledge Representation: Spaces, Trees, Features Unlimited
- Knowledge Representation: Language and Logic 1 Unlimited
- Knowledge Representation: Language and Logic 2 Unlimited
- Knowledge Representation: Great Debates 1 Unlimited
- Knowledge Representation: Great Debates 2 Unlimited
- Basic Bayesian Inference Unlimited
- Graphical Models and Bayes Nets Unlimited
- Simple Bayesian Learning 1 Unlimited
- Simple Bayesian Learning 2 Unlimited
- Probabilistic Models for Concept Learning and Categorization 1 Unlimited
- Probabilistic Models for Concept Learning and Categorization 2 Unlimited
- Unsupervised and Semi-supervised Learning Unlimited
- Non-parametric Classification: Exemplar Models and Neural Networks 1 Unlimited
- Non-parametric Classification: Exemplar Models and Neural Networks 2 Unlimited
- Controlling Complexity and Occam’s Razor 1 Unlimited
- Controlling Complexity and Occam’s Razor 2 Unlimited
- Intuitive Biology and the Role of Theories Unlimited
- Learning Domain Structures 1 Unlimited
- Learning Domain Structures 2 Unlimited
- Causal Learning Unlimited
- Causal Theories 1 Unlimited
- Causal Theories 2 Unlimited
Instructor
Massachusetts Institute of Technology
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