Graduate Certificate

Artificial Intelligence
& Machine Learning

Tickle College of Engineering

Program Overview

The University of Tennessee, Knoxville, offers an online Graduate Certificate in Artificial Intelligence and Machine Learning intended for those who currently hold at least a bachelor’s degree in a computing or related field, or for those currently enrolled in an MS or PhD program in engineering.

Credit Hours


Cost Per Credit Hour*

In-State $815

Out-of-State $890



Admission Terms

Fall, Spring, Summer

*Cost per credit hour is an estimate based on maintenance and university fees. Some programs may have additional course fees. Please contact your department for additional information on any related fees, and visit Tuition and Fees in Detail at One Stop.


The Artificial Intelligence and Machine Learning Graduate Certificate is offered by the Department of Electrical Engineering and Computer Science. The applicants are expected to have a Bachelor’s degree in a computing or related field with an undergraduate GPA of 3.00 or better. Recommended background knowledge includes Programming, Linear Algebra, and Probability Theory. Applicants lacking a programming background are encouraged to take COSC 505 before commencing the certificate. Applicants may be admitted to the certificate or complete the certificate as part of an MS or PhD. 

The certificate will help you build a strong foundation in core components of artificial intelligence and machine learning and prepare for industry or academic positions as a leader in the field. 

Featured Courses

In addition to two core courses, students will take at least three technical concentration courses, such as the following: 

COSC 522: Machine Learning

Core Course: Theoretical and practical aspects of machine learning techniques related to pattern recognition. Statistical methods studied include Bayesian and linear classifiers, support vector machines, neural networks, and unsupervised learning. Syntactic methods include grammatical inference, string matching and Markov chains. Ensemble methods include random forests, adaptive boosting, and classifier fusion.

IE 565: Applied Data Science

Technical Course: An introduction to applied data science including machine learning and data mining tools. Topics include supervised and unsupervised algorithms, techniques for improving model performance, evaluation techniques and software packages for implementation. Emphasis will be put on real-world applications in various domains including healthcare, transportation systems, etc.

COSC 529: Autonomous Mobile Robots

Technical Course: Introduction to key artificial intelligence issues involved in the development of intelligent robotics. Methods studied include locomotion, navigation, sensing, localization, mapping, exploration, path planning, robot learning, uncertainty, and multi-robot systems.

COSC 523: Artificial Intelligence

Core Course: Theoretical and applied aspects of artificial intelligence. Course topics include problem solving and search, knowledge representation and reasoning, decision-making under uncertainty, machine learning, and multi-agent systems.

Ready to advance in Artificial Intelligence & Machine Learning?