Course Content :-

• Foundations of Inductive Learning.
• Knowledge Representation: Spaces, Trees, Features.
• Knowledge Representation: Language and Logic.
• Knowledge Representation: Great Debates.
• Basic Bayesian Inference.
• Graphical Models and Bayes Nets.
• Simple Bayesian Learning.
• Probabilistic Models for Concept Learning and Categorization.
• Unsupervised and Semi-supervised Learning.
• Non-parametric Classification: Exemplar Models and Neural Networks.
• Controlling Complexity and Occam's Razor.
• Intuitive Biology and the Role of Theories.
• Learning Domain Structures.
• Project Presentations.