Lecture notes

Introduction

• Cognitive modeling and programming
• Basic Java concepts
• A little Unix

Basic Java

• Inroduction to methods
• Data and types
• Arguments, parameters, and return values
• The main method
• Operators and conditionals
• Local variables and assignments
• Arrays and for loops
• Comments
• Constants
• Method definitions
• Type conversion
• Classes and objects
• Constructors
• Review of variables
• Some conventions for code
• Class hierarchies
• Static variables and methods
• Event handling
• Graphics
• Reading from and writing to files
• Applets
• Threads and timing
• Parameter passing

Learning

• Perception, action, learning, evolution
• Reinforcement learning: basic concepts
• Reinforcement learning: details
• Reinforcement learning: examples
• Reinforcement learning: exploitation and exploration
• Reinforcement learning in Smarts
• Reinforcement learning: reference
• Local and distributed representations
• Neural networks 1
• Neural networks 2
• Neural networks and reinforcement learning
• Implementing Q learning with neural networks

Evolution

• Evolutionary computation and genetic algorithms
• Genetic algorithms and artificial life
• Implementing evolution

© 2004. Indiana University and Michael Gasser. www.indiana.edu/~gasser/Q530/Notes/index.html 8 Dec 2004