The Center for Intelligent Systems and Machine Learning (CISML) is a collaboration of faculty from three colleges and eight academic departments at the University of Tennessee (UT) and is part of the College of Engineering on the Knoxville campus. Beginning as a simple faculty initiative to create a unified curricula, CISML evolved (in October 2010) into an official UT research center that studies the theory and application of intelligent systems and machine learning. In addition to UT faculty, the Center's research staff is comprised of eight experts from Oak Ridge National Laboratory (ORNL).
CISML's focus is on designing computer-based systems that exhibit intelligent behavior, operate autonomously, and adapt to environmental changes. Examples of the diverse research activities in this area include pattern recognition, robotics, artificial intelligence, biologically-inspired cognitive architectures, bioinformatics, and data mining, to name a few.
Are you a Masters Student looking for an assistantship?
The GRAMS program, coordinated by CISML, is an assistantship program funded by the Oak Ridge National Laboratory (ORNL) and the Science Alliance (a UT Center of Excellence). The program is restricted to master students who are US citizens and who are currently enrolled in full-time graduate study at the University of Tennessee. For more information visit the GRAMS website here.
MEET Friday ~ Dr. Marco Santello
Dr. Marco Santello will be the guest speaker at the Special Health Engineering Colloquium on Friday ~ October 11th ~ 404 Min Kao.
Dr. Santello is from Arizona State University in Tempe, Arizona in the School of Biological and Health Systems Engineering. He will be speaking on Neural control of the hand: Interactions between feedback and feedforward mechanisms. Coffee/Juice/Bagels/Fresh Pineapple provided at 8:30am. Colloquium ~ 9AM-10AM.
Geog 611 (Spring 2014)
Professor Nicholas Nagle from the Department of Geography will be offering a doctoral seminar next term entitled Spatial Data Fusion and Classification. The seminar will meet Wed evenings from 5:05-7:45pm. This course is about learning from geospatial data, and especially about land cover classification. It will cover tools from Computer Science and Machine Learning, and informed by the geographer's domain knowledge of human and physical processes. Geog 611 will be offered at the same time as the 2014 IEEE Geospatial Data Fusion Contest. A centerpiece of the course will be a collaborative entry to the contest by the class. The contest organizers will release multiple sources of data around February 1st, at which time the class will race against other teams from around the world to have the best project before the competition closes at the start of May. Graduate students in Geography, EPS, EECS, and Statistics are encouraged to enroll.
Prerequisities: Experience with data analysis in an environment such as R, Matlab, Python, ArcGIS, ERDAS, or similar programming language. For further information, contact Prof. Nicholas Nagle (965-074-6035, nnagle [at] utk [dot] edu). Click here for further details.