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IT3708 - Subsymbolic Methods in AI, Fall 2006 |
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Welcome to the IT3708 web page!This course aims to introduce the students to so-called subsymbolic methods and concepts in AI, particularly artificial neural networks and evolutionary algorithms. On these pages you will find relevant static information regarding the course. It's Learning is also used to a certain extent, so check those pages as well. Students who follow the course should check the news section regularly. Latest News
Read more news here. Course OverviewThis course introduces several artificial intelligence methods that involve more quantitative calculation than symbol manipulation. The two key sub-symbolic methodologies that will be covered are evolutionary algorithms (EA) and artificial neural networks (ANN), both of which are derived from biological principles. EAs use the evolutionary biological metaphor of Darwinian natural selection as the basis for stochastic parallel search through complex fitness landscapes. This powerful search technique has been applied to a host of problem domains, such as task-scheduling, controller design, pattern recognition, robotics, immunology, economics and machine learning. In addition, EAs are commonly used in artificial life (alife), an exciting new interdisciplinary area with strong connections to biology, chemistry, mathematics, computer science and engineering. We will focus on two types of EAs: genetic algorithms (GA) and genetic programming (GP). Neural networks are powerful machine-learning tools abstractly based on the behaviors of collections of neurons in the brain. They are exceptionally useful in learning general concepts from examples, in either a supervised mode (i.e. the system is told which class each example belongs to) or unsupervised situations, where the ANN has to cluster the examples based on similarity. The distributed nature of information in neural networks makes them fundamentally different from standard AI knowledge representations and very popular as models of memory and as tools for complex data-analysis tasks in a wide variety of domains. People
Lecturer: Keith Downing (keithd<at>idi.ntnu.no)
Teaching Assistant: Boye Annfelt Høverstad (hoversta<at>idi.ntnu.no) |
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