University of Tennessee Machine Learning Degree

Program Overview

Tickle College of Engineering

The University of Tennessee’s Master of Science in Computer Science program provides students with advanced investigative and problem-solving skills to launch careers in one of the fastest-growing fields in technology. This comprehensive graduate program offers optional concentrations including Data Mining and Intelligent Systems, which aligns closely with machine learning applications.

Students benefit from internationally renowned faculty who lead cutting-edge research across multiple computer science disciplines, ensuring exposure to the latest developments in artificial intelligence and machine learning technologies.

Machine Learning Focus and Certificate Options

While the MS in Computer Science doesn’t require a specific concentration, students interested in machine learning can pursue the Data Mining and Intelligent Systems track or complement their degree with the Artificial Intelligence and Machine Learning Graduate Certificate.

The certificate program specifically targets students with backgrounds in computing, programming, linear algebra, and probability theory, providing specialized training in theoretical and practical machine learning applications. Featured courses include COSC 522 Machine Learning, which covers pattern recognition techniques, and CPSC 523 Artificial Intelligence, exploring problem-solving, knowledge representation, and multi-agent systems.

Curriculum

Required coursework for the Data Mining and Intelligent Systems track include:

COSC 522 – Machine Learning
This course explores machine learning methods for pattern recognition, focusing on both theoretical and practical aspects. Topics include Bayesian and linear classifiers, support vector machines, neural networks, unsupervised learning, and ensemble methods like random forests and adaptive boosting. Students will need knowledge of programming, probability, and linear algebra.

COSC 523 – Artificial Intelligence
This course introduces students to core AI concepts, including problem-solving, search strategies, knowledge representation, decision-making, and multi-agent systems. It blends theory and applied aspects, equipping students with practical skills to develop AI-based solutions. Programming, algorithms, linear algebra, and probability theory are recommended background knowledge.

COSC 525 – Deep Learning
The course focuses on deep neural networks for handling high-dimensional data. Students will study supervised and unsupervised models such as CNNs, autoencoders, GANs, and RNNs. A background in machine learning and Python is essential.

COSC 526 – Data Mining and Analytics
Students learn how to analyze big data using practical and theoretical approaches. They will gain experience with modern data science tools, MapReduce, and clustering methods, and apply these skills to real-world data sets. Python programming is required.

COSC 524 – Natural Language Processing
This course provides an overview of natural language processing theory and applications. It covers major NLP algorithms and explores how computational systems process text. Students should have an understanding of algorithms and data structures.

COSC 545 – Fundamentals of Digital Archeology
Students learn to recover, analyze, and present operational data while tackling challenges like missing observations and unreliable values. This course combines critical thinking with practical exercises. Hands-on assignments include data retrieval, context recovery, and report writing.

COSC 557 – Visualization
This course teaches graphical techniques to reveal patterns in scientific and engineering data. Topics include visualization of scalar, vector, and tensor fields, as well as time-varying data and advanced light transport. Prior knowledge of data visualization is helpful.

ECE 517 – Reinforcement Learning
The course covers reinforcement learning principles, including Markov decision problems, dynamic programming, and temporal difference learning. It also considers hardware and software design for RL applications. Machine learning knowledge is recommended.

ECE 574 – Computer Vision
Students study principles of computer vision, including segmentation, feature detection, depth recovery, motion tracking, and object detection. Prior coursework in image processing is recommended.

COSC 530 – Computer Systems Organization
This course examines computer architecture topics like memory hierarchies, pipelining, multiprocessors, and instruction-level parallelism. Students also study RISC/CISC designs and simulation tools. Coursework in computer architecture is advised.

COSC 533 – Cloud and Web Architectures
Students explore designing applications for the Internet and the web, focusing on protocols, security, synchronization, and interactivity. Coursework also covers client-server architecture and advanced web technologies. Prior experience with computer interfaces and operating systems is helpful.

COSC 534 – Network Security
The course investigates core Internet and wireless technologies, security vulnerabilities, and best practices. It includes hands-on exercises in network security and exploit prevention. Prior coursework in networking is suggested.

COSC 540 – Advanced Software Engineering
This course covers advanced software development processes and management techniques for large projects. Students learn about design, maintenance, and testing strategies to ensure project success.

COSC 558 – User Interfaces
The course provides in-depth knowledge on designing and evaluating user interfaces. Students learn about interface architectures, specification, event abstraction, and component implementation. Software engineering background is recommended.

COSC 559 – Human-Computer Interaction
This course covers the fundamentals of HCI, examining how human factors affect interface design and usability. Topics include perception, cognition, and user-centered design methodologies. Software engineering knowledge is helpful.

COSC 561 – Compilers Construction
Students learn compiler design, including scanning, parsing, intermediate code generation, type systems, and optimizations. Prior programming, architecture, and OS coursework is recommended.

COSC 562 – Operating Systems: Design and Implementation
This course focuses on the design and implementation of operating systems, covering virtual memory, interrupts, system calls, and process management. Prior coursework in operating systems is beneficial.

COSC 563 – Mobile and Ubiquitous Computing
Students learn about the design and implementation of mobile and ubiquitous computing systems, including IoT applications. Topics include security, sensors, and interaction in augmented environments. Prior coursework in computer interfaces and operating systems is suggested.

COSC 565 – Databases and Scripting Languages
This course introduces database theory and query formation along with an overview of scripting languages and their integration with databases. Prior coursework in computer programming is recommended.

COSC 566 – Software Security
Students study software vulnerabilities, exploitation techniques, and secure development practices. The course includes hands-on exercises to identify and avoid vulnerabilities. Networking coursework is recommended.

COSC 569 – Human Factors in Cybersecurity
This course examines the impact of HCI on cybersecurity, including authentication, secure communication, and inclusivity in design. Coursework helps students develop a human-aware mindset in security design. Prior experience in computer security is helpful.

COSC 581 – Algorithms
Students analyze algorithms for sorting, searching, graphs, dynamic programming, and approximation. The focus is on algorithm analysis for efficient design. Prior coursework in algorithms is recommended.

COSC 583 – Applied Cryptography
The course covers cryptographic techniques, underlying math, and practical applications. Hands-on projects reinforce understanding of secure communication. Prior coursework in security and algorithms is helpful.

ECE 553 – Computer Networks
This course provides an in-depth overview of computer networks from the application to the physical layer. Topics include sockets, routing, transport protocols, and practical programming assignments.

ECE 559 – Secure and Trustworthy Computer Hardware Design
The course explores secure hardware design, including attack models, SCA attacks, and hardware Trojan detection. Students complete practical assignments to enhance hardware security knowledge. Prior hardware design experience is recommended.

ECE 569 – Mobile and Embedded Systems Security
Students learn about security for mobile and IoT devices, including secure programming and side-channel attacks. Coursework includes hands-on projects and group research. Prior experience in computer security is helpful.

Curriculum details available here: https://catalog.utk.edu/preview_program.php?catoid=52&poid=32545

Cost

Graduate tuition for the 2025-26 academic year is $13,720 for in-state students and $32,208 for out-of-state students, with additional fees for engineering courses. The total estimated cost of attendance, including housing, food, and personal expenses, reaches $40,216 for in-state students and $58,704 for out-of-state students.

The Tickle College of Engineering awards over $1 million annually in scholarships to qualified students, though specific funding details for graduate programs aren’t provided in the documentation.

More tuition details here: https://onestop.utk.edu/billing-payments/cost-of-attending-ut-graduate-student/

Career Outcomes and Industry Applications

Graduates can pursue high-demand roles including computer and information research scientist, software developer, computer systems analyst, mobile application developer, and specialized positions like artificial intelligence engineer or data scientist.

The program prepares students for careers involving algorithm development, code writing, cybersecurity, and advanced machine learning implementations across various industries.

Students gain access to exceptional facilities including the Global Computing Lab and Innovative Computing Lab, plus research opportunities through partnerships with Oak Ridge National Laboratory, providing hands-on experience with cutting-edge technologies that directly translate to professional success.