# This file contains courses and seminars you regularly or occasionally teach. # # Each entry in this file is formatted as follows: # # %TI Course number, Title of course # %AU Your name, name of other faculty who teach this course # %AV URL for this course # %YR Year (and quarter) in which course will (tentatively) be next offered (optional) # %AB Georgia Tech course catalog entry for this course (or equivalent) # # Each field should be on one line, except %AB which can run over # several lines. You may use mailto:YOUR-EMAIL-ADDRESS for the URL # in the %AV field if the course does not have a Web page. %TI CS3361, Introduction to Artificial Intelligence %AU AI Faculty %AB Introduction to fundamental topics in artificial intelligence, including cognitive modelling, knowledge representation and inference, search, problem solving and planning, natural language processing, learning, expert systems, vision, and robotics. %TI CS 4324, Intelligent Robotics and Computer Vision %AU AI/GVU Faculty %AB Methodologies for embedding artificial intelligence in robotic systems. Topics include robot task and assembly planning, autonomous navigation, sensor-based robotic systems, supporting low-level visual processing, perceptual organization, and model-based vision. %TI CS 4331, Problem Solving and Learning %AU AI Faculty %AB Fundamental concepts and methods in knowledge-based problem solving and learning, including knowledge representation and organization, planning, knowledge-based systems, knowledge acquisition, example-based learning and explanation-based learning. %TI CS 4344, Natural Language Understanding By Computer %AU AI Faculty %AB Methodologifes for designing systems that comprehend natural language. Topics include lexical analysis, parsing, interpretation, and generation of sentences; semantic representation, organization of knowledge, and inference mechanisms. %TI CS 4361, Design Project in Artificial Intelligence %AU AI Faculty %AB Directed study with individual faculty on specialized projects in AI, including the design and development of a software AI system. %TI CS 4730, Lisp Programming for Artificial Intelligence %AU AI Faculty %AB Introduction to computer programming in Common LISP, with a focus on preparing the student for using Common LISP and AI programming techniques to solve problems in AI. %TI CS 6361, Artificial Intelligence %AU AI Faculty %AB Graduate level introductory course in AI, covering several topics ranging from knowledge representation, planning, search, and other fundamental of AI, to selected topics in natural language processing, learning, problem solving, computer vision and robotics. %TI CS 6362, Applications of Artificial Intelligence %AU AI Faculty %AB Introduction to real world applications of AI. Topics include robotics, computer vision, expert systems, and neural networks. Not for CS Ph.D. students. %TI CS 6364, Hypermedia %AU AI/GVU Faculty %AB Introduction to hypermedia tools, methods and design. %TI CS 6398, Design and Analysis of Educational Software %AU Mark Guzdial %AV http://www.cc.gatech.edu/gvu/people/Faculty/Mark.Guzdial/cs6398.html %YR Spring 1996 %AB Focus on the issues surrounding (1) designing educational software (e.g., content, structure, educational philosophy, making it work in school cultures) and (2) analyzing that software in actual use (e.g., issues of gathering data in real classrooms, analyzing log file data). %TI CS 7321, Low Level Computer Vision %AU AI Faculty %AB Introduction to computer vision and machine perception, and extracting symbolic and environmental information from images. Topics include paradigms, feature extraction, perceptual organization, perspective, motion, stereo, color, and texture. %TI CS 7322, High Level Vision %AU AI Faculty %AB Machine vision systems using AI and model-based techniques. Topics include architectures, object models, indexing and matching, hypothesis and uncertainty management, constraints, and active sensing. %TI CS 7323, Autonomous Robotics %AU Ron Arkin %AV http://www.cc.gatech.edu/aimosaic/faculty/arkin/cs7323.html %YR Spring 1996 %AB Designing intelligent autonomous robotic systems, with a special emphasis on neuroscientific and cognitive models of behavior. %TI CS 7331, Problem Solving %AU AI Faculty %AB Fundamental concepts and methods in knowledge-based problem solving, including knowledge representation and organization, planning, inference mechanisms, control architectures, design, explanation, and knowledge acquisition. %TI CS 7332, Case-Based Reasoning %AU AI Faculty %AB Case-based reasoning is a kind of analogical reasoning and an alternative method for building expert systems. Topics include case representation, indexing, and retrieval, adaptation, interpretive case-based reasoning, the cognitive model case-based reasoning implies, and its implications for creativity, decision aiding, and education. %TI CS 7341, Conceptual Information Processing %AU AI Faculty %AB In-depth introduction to the conceptual approach to language, understanding, inference and reasoning. Topics include knowledge representation, inference and causality, conceptual analysis of natural language, story generation, explanation, memory, learning and integrated processing. %TI CS 7342, Knowledge Structures for Machine Intelligence %AU AI Faculty %AB A study of the knowledge and inferences necessary for understanding and problem solving; knowledge organization; representation of episodes; question answering; reconstructive memory. %TI CS 7343, Machine Learning %AU AI Faculty %AB Fundamental issues in Machine Learning, including the algorithmic, psychological, philosophical and methodological foundations of the field. Topics include empirical or inductive learning, concept learning, learnability theory, analogical and case-based learning, and explanation-based learning. %TI CS 7344, Natural Language Understanding %AU Kurt Eiselt %AV http://www.cc.gatech.edu/aimosaic/faculty/eiselt/cs7344.html %YR Winter 1996 %AB Methodologies for designing systems that comprehend natural language. Topics include lexical analysis, parsing, and interpretation of sentences; semantic representation; organization of knowledge; and inference mechanisms. %TI CS 7360, Advanced AI System Development %AU AI Faculty %AB Study of advanced programming methodologies for AI, including data-driven programming, agenda control, deductive information retrieval, discrimination networks, production systems, frames, and chronological and dependency-directed backtracking. %TI CS 7511, Cognitive Modeling (4 units) %AU AI/CogSci Faculty %AB This is a hands-on course covering a range of computational modeling methodologies. It discusses how to build a model for a cognitive theory and evaluate it. It is typically team-taught. It does not require an extensive programming background, but is targetted at students in their second year and above. %TI CS 8113, Design and Analysis of Educational Software %AU AI/EdTech Faculty %AB Focus on the issues surrounding (1) designing educational software (e.g., content, structure, educational philosophy, making it work in school cultures) and (2) analyzing that software in actual use (e.g., issues of gathering data in real classrooms, analyzing log file data). %TI CS 8113R, ISyE 8100B, LCC 6003B. Educational Technology %AU AI/EdTech Faculty %AB Survey of existing theoretical approaches to learning, specific technologies, and resulting interaction styles. Topics include microworlds, constructionism, intelligent tutoring systems, student modelling, interactive learning environments, coaching/apprenticeship learning, collaborative learning, multimedia/hypermedia. %TI LCC 5791, Cognitive Perspectives %AU Nancy Nersessian %AV mailto:nancyn@cc.gatech.edu %YR Winter 1997 %AB The focus of the course will be on cognitive models of science proposed by philosophers. We will address such questions as : by constructing cognitive models can we better understand how scientists devise and execute real world and thought experiments, construct arguments, create concepts, invent and use mathematical tools, communicate ideas and practices, and train practitioners? Can theories and methods in the cognitive sciences provide a means for reconstructing historical "discovery processes"? What area(s) of cognitive science offer the most potnetial for fruitful analyses: AI, psychology, cognitive neuroscience? What is the relation between cognitive and social models of science?