Brown CS: Artificial Intelligence artificial intelligence at Brown University is concerned with theoretical and empirical studies is comprised of members from both the computer science and the http://www.cs.brown.edu/research/ai/
University Courses In Computer And Information Science Introductory; Algorithms; artificial intelligence; Automata Theory and Formal Models of Security (from NYU); Other Courses in computer and Information science; http://www.cis.temple.edu/courses.html
SCMS > Computer Science: BCMS Programmes a study of the problems and techniques of artificial intelligence with more psychology in addition to the School s core of computer science, mathematics, and http://www.cs.waikato.ac.nz/genquery.php?linklist=CS&linktype=folder&linkname=BC
Computer Science Courses translation, program execution time, computer networks, parallel computation, computability and artificial intelligence. Impact of computer science on modern http://www.cs.trinity.edu/About/The_Courses/catalog/catalog.html
Extractions: MAURICE L. EGGEN, Ph.D., Associate Professor THOMAS E. HICKS, Ed.D., Associate Professor JOHN E. HOWLAND, Ph.D., Professor AARON H. KONSTAM, Ph.D., Professor J. PAUL MYERS, JR., Ph.D., Associate Professor GERALD N. PITTS, Ph.D., Professor; Chair RONALD E. PRATHER, Ph.D., Caruth Distinguished Professor of Computer Science The requirements for the degree of Bachelor of Science with a major in Computer Science are as follows: I. The common curriculum II. Departmental requirements: 42 semester hours of computer science including: A. The undergraduate core: CSCI 311, CSCI 312, CSCI 314, CSCI 316, CSCI 320, CSCI 324, CSCI 325, CSCI 364, CSCI 374 B. One course chosen from each of the following areas: SYSTEMS: CSCI 341, CSCI 342, CSCI 346, CSCI 349 APPLICATIONS: CSCI 351, CSCI 353, CSCI 357, CSCI 358, CSCI 373 THEORY: CSCI 361, CSCI 363, CSCI 365, CSCI 367, CSCI 368 C. Senior Thesis (CSCI 378) or Senior Software Project (CSCI 376). D. Sufficient computer science electives to total 42 semester hours. III. Additional requirements include MATH 411 and two of the following courses: MATH 312, MATH 320, MATH 323, PHIL 310.
Computer Science And Artificial Intelligence (MRes) The MRes is unlike traditional taught Masters degrees in that it is designed for graduates with computer science and artificial intelligence degrees who have http://www.sussex.ac.uk/informatics/1-3-2-2-7-7.html
Extractions: The MRes is unlike traditional taught Masters degrees in that it is designed for graduates with Computer Science and Artificial Intelligence degrees who have already decided that they want a career in research. Students are attached to one of our successful research groups and receive personal tutoring and supervision in Evolutionary and Adaptive Systems, Neural Networks, Computer Vision, Natural Language Processing, Human Centred Computing Systems, or Distributed Programming Languages. Programme Objectives The emphasis is on research training and four of the six taught modules involve skill-based learning while the remaining two modules involve learning specialised knowledge associated with the students chosen area of research. This kind of research training has the following main advantages:
Artificial Intelligence for Computing Machinery (ACM) Special Interest Group on artificial intelligence from 197779, and served as Executive Editor of Cognitive science from 1983-86 http://www.cs.washington.edu/homes/lazowska/cra/ai.html
Extractions: NEC Research Institute Artificial Intelligence (AI) is the key technology in many of today's novel applications, ranging from banking systems that detect attempted credit card fraud, to telephone systems that understand speech, to software systems that notice when you're having problems and offer appropriate advice. These technologies would not exist today without the sustained federal support of fundamental AI research over the past three decades. Although there are some fairly pure applications of AI such as industrial robots, or the Intellipath TM Autonomous vehicles: A DARPA-funded onboard computer system from Carnegie Mellon University drove a van all but 52 of the 2849 miles from Washington, DC to San Diego, averaging 63 miles per hour day and night, rain or shine; Computer chess: Deep Blue , a chess computer built by IBM researchers, defeated world champion Gary Kasparov in a landmark performance; Mathematical theorem proving: A computer system at Argonne National Laboratories proved a long-standing mathematical conjecture about algebra using a method that would be considered creative if done by humans;
U Of Rochester CS Research artificial intelligence. Neural Models of Behavior; Center for Visual science (CVS Architecture, RunTime, and Compiler Integration for High-Performance Computing; http://www.cs.rochester.edu/research/
Extractions: Home directions news Department ... related depts. URCS Research In this page: AI Systems Theory See also: Technical Reports Seminars/Talks In-progress Ph.D. Dissertations Undergraduate Research Projects ... URCS Facilities Research Overview ARCH: Architecture, Run-Time, and Compiler Integration for High-Performance Computing
AI At Victoria University artificial intelligence at Victoria. courses at Victoria. Comp423, Intelligent agents (1/3, 2004). Comp424, Neural systems (not in 2004). computer vision. http://www.mcs.vuw.ac.nz/research/ai/
Extractions: (by arrangement only) post-graduate students Richard Mansfield (PhD student with Marcus) theoretical biology: competition between species Phillip Boyle (PhD student with Marcus) evolving autonomous agents Tim Field - MSc with Marcus: completed 2004 robotics Jerome Dolman (MSc with Marcus) Exploring Mechanisms of Neural Development through Parameterised Search Huayang 'Jason' Xie (MSc student with Pondy and Mengjie) speech recognition Mukhlis Matti (MSc student with Pondy) clustering algorithms Will Smart (MSc student with Mengjie) genetic programming staff Peter Andreae reinforcement learning clustering Marcus Frean machine learning complex adaptive systems theoretical biology Mengjie Zhang data mining machine learning genetic programming neural networks computer vision Xiaoying Gao (Sharon) information extraction from the web knowledge-based systems machine learning Tony Vignaux Bayesian methods forensic evidence
The 2003 International MultiConference Conference on Imaging science, Systems, and 03 The 2003 International Conference on artificial intelligence. 2003 International Conference on Internet Computing. http://www.ashland.edu/~iajwa/conferences/
MIT Artificial Intelligence Lab The artificial intelligence Laboratory has been an active entity at MIT in one form or another since at least 1959. Our goal is http://www.ai.mit.edu/
Extractions: On July 1, 2003, the MIT AI Lab and LCS merged to become MIT CSAIL The Artificial Intelligence Laboratory has been an active entity at MIT in one form or another since at least 1959. Our goal is to understand the nature of intelligence and to engineer systems that exhibit intelligence. We are an interdisciplinary laboratory of over 200 people that spans several academic departments and has active projects ongoing with members of every academic school at MIT. Our intellectual goal is to understand how the human mind works. We believe that vision, robotics, and language are the keys to understanding intelligence, and as such our laboratory is much more heavily biased in these directions than many other Artificial Intelligence laboratories [Academics] [AI Lab Home Page] [Contact Us] [Events] ... [Visiting the Lab] MIT Artificial Intelligence Laboratory, 200 (545) Technology Square, MIT Building NE43, Cambridge, MA 02139 USA, Tel: (617) 253-6218 webmaster: annika
SCHOOL OF COMPUTER SCIENCE/Carnegie Mellon University Wing Professor CSD, and new head of the computer science Department (CSD) tells why she is known as Students from the School of computer science, Take a tour of SCS http://www.cs.cmu.edu/
Extractions: News and Events: Features: Jeannette M. Wing, Professor CSD, and new head of the Computer Science Department (CSD) tells why she is known as Dragon Lady in this new interview by Women@SCS. Wing is highly regarded for her outstanding contributions in research, teaching, and administrative service to the college. Full interview
Berkeley CS Division Home Page The computer science Division. computer science Division Office. University of California Department of Electrical Engineering and computer sciences. University of California, Berkeley http://www.cs.berkeley.edu/
Networked Computer Science Technical Reference Library Networked computer science Technical Reference Library NCSTRL (Networked computer science Technical Reference Library) is a collection of legacy and current technical reports from over 100 http://rdre1.inktomi.com/click?u=http://www.ncstrl.org/&y=02DD48352F948907&a
Research Index Logo, Department of computer science, Thomas M. Siebel Center for computer science, 201 N. Goodwin, Urbana, IL 618012302. The Department http://www.cs.uiuc.edu/research/
AILab, University Of Zurich artificial intelligence Laboratory Department of Information Technology University of Zürich. Director Prof. Dr. Rolf Pfeifer. Department http://www.ifi.unizh.ch/ailab/
AIRVL, University Of Minnesota Intelligent Transportation Systems; MinDART Minnesota Distributed Autonomous Robotics University of Minnesota Department of computer science and Engineering. http://www.cs.umn.edu/Research/airvl/
DTAI - Machine Learning Within the field of artificial intelligence, machine learning occupies a as there can be no intelligence unless it as that offered by computational logic, both http://www.cs.kuleuven.ac.be/~ml/index-E.shtml
Extractions: ML DTAI Dept. of Computer Science Faculty of Engineering ... K.U.Leuven [NEDERLANDS] ENGLISH Machine learning ( ML ) is concerned with building agents that improve their performance on specific tasks by accumulating and processing experience. Within the field of artificial intelligence, machine learning occupies a central position as there can be no intelligence unless it is capable of learning. Data Mining The aim of data mining is to find regularities in (possibly large) sets of data. These regularities can be used either for predictive purposes, or for descriptive purposes. As an example of the former, if the data is about patients, their symptoms and diagnosis, one can combine the symptoms of a new patient with the induced regularities to propose a diagnosis. As an example of the latter, in the same domain, an induced regularity describes a piece of scientific knowledge. If previously unknown to the medical doctors, it constitutes a piece of new knowledge which enhances the understanding of the domain. Inductive Logic Programming Classical data mining is suitable for learning patterns from data with a simple structure (typically a single relation where each tuple describes an example). However, the emphasis of our research lies on the development of algorithms that are able to induce regularities in structured domains (e.g. in biochemistry). This requires the use of an expressive representation language such as that offered by computational logic, both to describe the complex data used for learning and to express the patterns that are learnt. This has lead to the field of inductive logic programming, in which our group occupies a leading position.
