Skip to Main Content

The University of Tennessee

Center for Intelligent Systems and Machine Learning

Frequently Used Tools:


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.

Recent News

  • July 28, 2015 MATH 515 - Analytical Applied Math I

    Class 2:30 pm - 3:20 pm ~ MWF Ayres Hall 112
    Here is a short description: This course will mainly focus an asymptotic and perturbation methods. We will start with elementary examples using nonlinear equations and linear algebra, before moving on to problems involving integrals and differential equations. Topics will include boundary layer theory, WKB theory, and the method of multiple scales. If you are have questions, please contact: Tim Schulze ~ schulze [at] ~ Recommended Background: Courses in advanced calculus, linear algebra, and differential equations.


    July 30th – 6:30PM ~ CIRRUSPATH OFFICE ~ 117 Center Park Drive ~ FEATURE SPEAKER: Blair Christian – Data Scientist for PYA Analytics ~

  • July 2, 2015 MATH 523 to be taught FALL 2015

    Dr. Vasileios Maroulas will be teaching Math 523 next semester (and Math 524 in the Spring). The series Math 523-4 is the core of any course and research topic related to SDEs, SPDEs, Computational Probability and Statistics, Mathematical Statistics, Data Sciences, and in general is the central topic of a huge list of modern research topics. The course will focus on the foundations of probability illustrated by many examples, and when it is necessary, applications will be mentioned. We will use Rick Durrett’s book on “Probability Theory and Examples” (4th edition).