Tennessee Tech Machine Learning Degree

Program Overview

Department of Computer Science

The Master of Science in Computer Science at Tennessee Tech is a 31-34 credit hour program designed to prepare students for careers in technology, research, or further doctoral study. The program can be completed in about 24 months and offers three pathways: thesis, project, or coursework-only options.

Each path includes core coursework, electives, and either a thesis, project, or independent study requirement.

Curriculum and Machine Learning Focus

The curriculum covers a wide range of topics in computer science, including artificial intelligence, data science, cybersecurity, and high-performance computing.

Students can work with faculty on machine intelligence and machine learning research, providing hands-on experience in this rapidly growing area. Course offerings like “Mathematics and Theory of Machine Learning,” “Data Mining,” and “Advanced Topics in Artificial Intelligence” allow students to develop specialized skills in machine learning.

Course options include:

Graduate Seminar

CSC 6910 – Graduate Seminar provides a platform for students to engage with current topics and research in computer science through presentations and peer interaction. Attendance and participation are required throughout the semester.

Core Theory

CSC 5400 – Analysis of Algorithms explores algorithm design and analysis, covering techniques for traversing and searching data structures, solving string and graph problems, and understanding computational complexity, including NP-hard and NP-complete classifications.

CSC 6240 – Mathematics and Theory of Machine Learning examines the mathematical foundations of machine learning, including linear algebra, probability, statistics, multivariate calculus, and complexity theory. The course also focuses on learning algorithms and their efficiency in terms of time, space, and sample complexity.

CSC 6400 – Advanced Analysis of Algorithms offers deeper insights into algorithmic analysis techniques and complexity classes, with emphasis on formal methods and approximation algorithms.

Parallel and Distributed Computing

CSC 5760 – Parallel Programming introduces parallel computing principles, including architectures and programming methods for shared/distributed systems and GPGPU. Students develop the skills to write efficient parallel algorithms.

CSC 5770 – Distributed & Cloud Computing covers foundational concepts in distributed systems, networking, cloud technologies, and the programming frameworks that support them.

CSC 6730 – Advanced Networking focuses on complex networking topics beyond standard curricula, including wireless and multimedia networks, advanced protocols, and research literature review.

CSC 6740 – Parallel and Distributed Algorithms emphasizes the design and analysis of algorithms suited for parallel and distributed computing environments.

CSC 6780 – Distributed Computing addresses key principles in distributed systems such as synchronization, replication, security, and file management.

CSC 6903 – Special Topics (related to Parallel and Distributed Computing) allows students to study advanced or emerging topics in this specialization under faculty guidance.

CSC 7560 – Advanced Networking and Next Gen Internet Protocols examines the history and future of Internet architecture, enabling students to understand its evolution and explore real-world network solutions.

CSC 7720 – Distributed Operating Systems studies OS concepts for distributed systems, including process management, file systems, security, and synchronization.

CSC 7750 – Topics in High-Performance Computing introduces techniques and technologies used in modern high-performance computing, focusing on parallelism and distributed systems.

Information Assurance and Security

DS 5125 – Computer Forensics and Investigation teaches investigative techniques used to collect and analyze digital evidence in cybersecurity contexts.

DS 5260 – Network Security and Forensics covers the tools and strategies required to identify and respond to network intrusions and cyber threats.

CSC 5575 – Cryptography/Network Security provides a strong foundation in cryptographic methods and their role in securing network communications.

CSC 5585 – Software and Systems Security integrates secure software engineering practices with automated testing and vulnerability assessment techniques.

CSC 6570 – Cloud Security Fundamentals and Practices explores secure cloud architecture design, addressing compliance and implementation of key security controls.

CSC 6575 – Internet Security focuses on securing Internet-based protocols and applications from various cyber threats.

CSC 6580 – Advanced Reverse Engineering delves into malware detection and analysis using both static and dynamic reverse engineering techniques.

CSC 6585 – Secure Software Development trains students to detect insecure coding patterns and implement program analysis tools for proactive software security.

CSC 6590 – Application Security teaches vulnerability identification and mitigation in programming environments, covering encryption, memory protection, and secure coding.

CSC 6903 – Special Topics (related to Information Assurance and Security) provides the flexibility to explore specialized security issues through advanced study.

CSC 7570 – AI Assisted Cyber Security blends artificial intelligence with cybersecurity, with a focus on adversarial attacks, malware detection, and federated learning.

CSC 7575 – Cyber Physical System Security discusses the security challenges in modern CPS domains like smart grids and autonomous systems.

Artificial Intelligence

CSC 5220 – Data Mining and Machine Learning introduces techniques for analyzing data and training intelligent systems, with hands-on experience in software analysis tools.

CSC 5240 – Artificial Intelligence surveys key AI domains including knowledge representation, reasoning, learning, and intelligent agent design.

CSC 5260 – Advanced Data Science & Applications emphasizes real-world data science through projects using Hadoop, visualization tools, and privacy-aware data practices.

CSC 6220 – Data Mining focuses on data preparation and mining techniques such as classification, clustering, and association rules.

CSC 6230 – Machine Learning teaches classification, feature selection, evaluation methods, and an introduction to reinforcement and unsupervised learning.

CSC 6260 – Advanced Topics in Artificial Intelligence covers emerging AI applications such as neural networks, natural language processing, and expert systems.

CSC 6903 – Special Topics (related to Artificial Intelligence) allows exploration of AI-focused subjects beyond the standard curriculum.

CSC 7210 – Anomaly and Intrusion Detection Systems investigates traditional and modern approaches for identifying anomalies in domains such as fraud and cybersecurity.

CSC 7240 – Intelligent Information Systems merges foundational AI with decision support and adaptive computing techniques for knowledge discovery.

More curriculum details available here: https://grad.catalog.tntech.edu/courses?cq=&sortBy=code&subjectCode=CSC&page=1

Outcomes

Graduates are equipped for roles in software development, data science, cybersecurity, and research labs, as well as for continued academic study in Ph.D. programs. The program is delivered in-person with supportive faculty and a friendly campus atmosphere.

Admission and Program Fit

Applicants typically need a bachelor’s degree in computer science, a GPA of 3.0 or higher, GRE scores, letters of recommendation, and relevant experience or research. International students must also meet English proficiency standards. This program is a good fit for those looking to advance in the computer science field or shift into specialized areas like machine learning.

Flexibility and Research Opportunities

Students can choose electives from a range of 5000, 6000, and 7000-level courses to match their interests, including options in data science, cybersecurity, and AI. The program’s strong research focus and collaboration with national labs create a dynamic learning environment.

The small program size and active Computer Science Graduate Student Club provide opportunities for networking, professional growth, and direct faculty mentorship.

Tuition

In-State

Approximately $11,366/year, totaling about $22,732 over two years

Out-of-State

Approximately $26,486/year, totaling about $52,972 over two years

This does not include additional fees or expenses such as books, supplies, and living costs.