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IT3708 - Subsymbolic Methods in AI, Spring 2012 |
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2012 Lectures: Thursdays, Kl 10:15 - 12:00 in Room F4 (First lecture on January 12, 2012)This course introduces students to subsymbolic methods and concepts in AI, including evolutionary algorithms (EAs), artificial neural networks (ANNs), swarm intelligence , artifical development and immune systems On these pages you will find relevant static information regarding the course.
We will use It's Learning for several aspects of the course, including student uploading of projects and essays. However, most course materials such as lecture notes, supplementary literature and homework assignments will only be available at THIS website. In addition, the instructor uses the it3708 mailing list (and not necessarily It's Learning) for important course announcements, so please subscribe to that list as soon as possible.
Latest NewsERROR IN NEWS FILE! Error on line 0 (0-based). Read more news here. Course OverviewThis course introduces several artificial intelligence methods that involve more quantitative calculation than symbol manipulation and are further 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. In previous versions of the course, the primary focus has been on EAs and NNs whilst introducing other sub-symbolic techniques. This spring, more focus is also given to the techniques: swarm intelligence, artificial development and immune systems. People
Lecturer and coordinator: Pauline C. Haddow (pauline<at>idi.ntnu.no)
Lecturer: Anders Kofod-Petersen (anderpe<at>idi.ntnu.no)
Teaching Assistant: Vinay Gautam(vkgautam<at>idi.ntnu.no) |
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