Fall 2008 Undergraduate & Graduate Special Topic Courses
CSC495A - Artificial Intelligence Programming (This course is piggybacked with the graduate version CSC591A) - Dr. St Amant
This course will introduce students to practical programming techniques and system design concepts appropriate for building complex AI systems. Material in the course complements the more theoretical approach taken in the Artificial Intelligence courses , CSC 420 and CSC 520. Students will learn the programming language Common Lisp through a series of programming assignments and will develop larger systems as part of a course project
CSC495K - Knowledge-Based Service Support Systems (This course is piggybacked with the graduate version CSC591k) - Dr. Jon Doyle
Provision and management of services to industries and individuals rely heavily on flexible application and adaptation of significant bodies of knowledge about the service domain, tasks, providers, and consumers. This course covers the concepts and techn ology available for capturing this knowledge in persistent and evolving corporate memories, and for using this knowledge to assist service providers in carrying out their tasks. The course covers methods of knowledge acquisition and representation involved in capturing expertise, and problem-solving methods relevant for rendering assistance in key types of service tasks. Students gain practical experience with the technologies a nd methods by building knowledge-based support systems as part of a term project, and gain theoretical experience through a set of readings from the literature.
CSC591A - Artificial Intelligence Programming -Dr. St Amant
This course will introduce students to practical programming techniques and system design concepts appropriate for building complex AI systems. Material in the course complements the more theoretical approach taken in the Artificial Intelligence courses, CSC 420 and CSC 520. Students will learn the programming language Common Lisp through a series of programming assignments and will develop larger systems as part of a course project
CSC591K - Knowledge-Based Service Support Systems - Dr. Jon Doyle
Provision and management of services to industries and individuals rely heavily on flexible application and adaptation of significant bodies of knowledge about the service domain, tasks, providers, and consumers. This course covers the concepts and techn ology available for capturing this knowledge in persistent and evolving corporate memories, and for using this knowledge to assist service providers in carrying out their tasks. The course covers methods of knowledge acquisition and representation involved in capturing expertise, and problem-solving methods relevant for rendering assistance in key types of service tasks. Students gain practical experience with the technologies a nd methods by building knowledge-based support systems as part of a term project, and gain theoretical experience through a set of readings from the literature.
CSC791T - Human Language Technologies - Dr. James Lester
Human Language Technologies (HLT) is the field of artificial intelligence that focuses on the automated understanding and generation of human language. With a broad array of applications in conversational interfaces, web search, document analysis, and te xt mining, HLT has become the subject of growing interest in both academia and industry. HLT tasks range from information retrieval, information extraction, question answering, and text mining to text-based and spoken dialog understanding. Variously know n as Natural Language Processing and Computational Linguistics, HLT has made significant strides in recent years with the introduction of probabilistic, statistical, corpus-based, and machine learning techniques.
This course presents the foundations of HLT and explores frontier HLT tasks. It introduces the principles and methods underlying the design of scalable computational models of human language with an emphasis on statistical frameworks, and it explores the issues bearing on the design of accurate and efficient solutions. It begins by introducing the fundamentals of language modeling, part-of-speech analysis, grammars, parsing, and semantic and pragmatic analyses, and then proceeds to information retrieval problems including text classification and text clustering. As time permits, it will also cover advanced topics such as open domain question answering, information extraction, text mining, natural language generation, and summarization. Cognitive and li nguistic phenomena will be considered throughout the course.
Course Prerequisites: Graduate standing in Computer Science, CSC 520 or the equivalent. Graduate students in Bioinformatics may make special arrangement
