Special Topics Courses

Fall 2026

CIS 4930 – Introduction to Machine Learning

Instructor: Mohammad Al-Saad

Description: Machine learning is a specialized area within artificial intelligence focused on enabling computer programs to autonomously enhance their functionality and efficiency by acquiring (learning) experience. The primary goals of this course are to equip students with a comprehensive introduction to machine learning methods and techniques, and to delve into the investigation of research problems within machine learning and its applications, which may lead to work on a project or a dissertation. The course is intended primarily for computer science and artificial intelligence students. Additionally, students from various fields who possess a keen interest and a robust background in artificial intelligence may also find this course interesting.

CIS 4930 – Cyber Adversarial Tradecraft

Instructor: Cheryl Resch

Description: The course introduces a theory of adversarial engagement and related game theoretical concepts. It addresses the theory and practice through conflict principles associated with both offense and defense along the dimensions of deception, physical access, humanity, economy, planning, innovation, and time. Students engage in weekly exercises putting these theories into practice in adversarial competitions. Students will be able to identify and employ these concepts in both offensive and defensive cyber activities.

CIS 4930 – Theory of Computation

Instructor: Meera Sitharam

Description: Introduction to theoretical computer science, the nature of automation, information, randomness and complexity.

For those interested in understanding the limits of AI from a mathematically principled perspective, or if you just liked the argument about why no computer can solve certain problems in finite time, such as the halting problem and various tiling problems, then this course could be of interest to you.

For those entering a graduate CS program it establishes a solid foundation.  I’ve had motivated undergrads with a strong analytical background take this course and do extremely well.

The course is about acquiring a way of thinking, based on computation, complexity, information and randomness: it will cover fundamental questions about the nature of automation, models of computation, computability, and how to classify problems by their computational complexity, simply by looking at their formal description. The answers to these questions form the very foundations of computer science, from programming language principles, to algorithm design.

Furthermore, the study of complexity is increasingly recognized as a fundamental area of mathematics and is a key ingredient in in physics and the natural sciences. 

Note that COP3530 Data Structures and Algorithms may be listed as an official prerequisite, and it helps if you had this prerequisite (in fact  COP 4533 Algorithm Abstraction and Design will be useful as well).

On the other hand, if you feel comfortable with formalizing and proving things, i.e.. you feel you have mathematical maturity (you are a math major or have had upper division math courses, for instance) you are probably ready. If you want confirmation, you should email   Meera Sitharam at sitharam@cise.ufl.edu

CIS 4930 – Math for Machine Learning

Instructor: Jingwei Sun

Description:  Mathematical foundations of machine learning. Topics include linear algebra, vector spaces, orthogonality, least squares, regularization, convex sets and functions, gradient-based optimization, principal component analysis, clustering, support vector machines, and fundamentals of neural networks. Emphasis on mathematical modeling, geometric interpretation, and analytical tools for understanding learning algorithms.

CIS 4930 – Enterprise Software Engineer Practices

Instructor: Pete Dobbins

Description: This course will introduce students to modern software engineering practices used to build software in large enterprises. Students will learn about frameworks and tools that help organizations with hundreds or even thousands of engineers collaborating to deliver software. Students will expand upon their knowledge of the Software Development Life Cycle to better understand how to contribute code to existing codebases, automate testing and deployment activities, and proactively monitor and support their software. Students will learn how to evaluate requirements from a business and customer perspective, ensuring that they contribute software that is impactful. These real-world skills will help students stand out as they pursue full-time software engineering opportunities and hit the ground running in their first industry jobs.

CIS 4930: Multimedia Expert Systems

Instructor: Jonathan Kavalan

Description: Understand the integrated design issues for multimedia expert systems, Survey of recent advances in multimedia expert systems.

Summer 2026

CIS 4930 – Technology Frontiers for Industry 5.0

Instructor:  Alexandre Gomes de Siqueira

Description: Technology Frontiers for Industry 5.0 is a forward-looking course for students who want to design the future of industry, not just code or automate it. Industry 5.0 puts humans back at the center—where people, AI, robots, and immersive systems collaborate ethically and responsibly in real-world environments. In this course, students learn from guest lectures by expert professors and researchers from universities and research centers around the world, gaining first-hand exposure to how emerging technologies are being explored and deployed across different countries, industries, and cultural contexts. Through real case studies, students work with cutting-edge ideas spanning AI, quantum, cyber-physical systems, immersive technologies, robotics, and advanced manufacturing, while building the critical thinking, ethical awareness, and communication skills needed to lead the next generation of human-centered industrial systems. Learn from global experts. Tackle real Industry 5.0 challenges. Design technology that puts humans first.

CIS 4930 – Introduction to Virtual Reality

Instructor: Alexandre Gomes de Siqueira

Description: This course explores the theory and practice necessary to develop effective immersive virtual environments as a medium to solve real-world problems and convey impactful messages. It discusses techniques for achieving real-time, dynamic generation of synthetic audio, visual, and haptic stimuli. It includes hands-on experience with head-mounted displays and other VR technologies. By the end of the course, as a final project, students will have designed and built a fully functional virtual environment.

CIS 4930 – Introduction to Machine Learning

Instructor: Fatemeh Tavasolli

Description: An applied introduction to machine learning. Students learn how to design, train, evaluate, and improve predictive models using real-world datasets and industry-standard tools in Python.

Topics include data preparation, supervised learning methods (such as linear models, classification, decision trees, and ensemble methods), model evaluation, regularization, and an introduction to neural networks. The course emphasizes understanding the full machine learning process, from preparing raw data to building and evaluating reliable models, through guided coding exercises and practical applications.

The final project requires students to design, develop, and present an end-to-end machine learning solution.

CIS 4930 – Brain Wave Interfaces

Instructor: Marvin Adujar

Description: This course explores the cutting edge of Human-Computer Interaction (HCI), focusing on new forms of interface that use passive measurements of neurophysiological states. We will delve into how we can use signals from the brain to understand cognitive states like workload and engagement, and even to control machines.

You will explore the foundational research in a range of related fields, including Neuroscience, Cognitive Psychology, and Computational Neuroscience. A key component of the course is a deep dive into non-invasive electroencephalography (EEG), a technology that allows us to measure brain activity. We’ll also cover the basics of other brain imaging techniques, such as near-infrared spectroscopy (fNIRS) and functional Magnetic Resonance Imaging (fMRI) and discuss their applications in computing.