All special topics are offered for 3 credit-hours and must be taken for a letter grade. Students are limited to 12 hours of Special Topics in CSC. Graduate classes may not be changed to S/U (credit-only). Undergraduate students who are in the ABM or CSC Honors may use the Graduate Request form to request enrollment.

CSC 591 – 001, Extended Reality and 3D Interaction

• Recommended pre-requisite: CSC 484/584
• Description: This course introduces students to the designs, technologies, and evaluation of Extended Reality (XR), including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Students will explore the fundamentals of 3D interaction, spatial user interfaces, and immersive experience design. Topics include tracking and sensing, display technologies, interaction techniques, user experience evaluation, and application development using XR platforms. Through hands-on group projects, students will gain practical skills in creating interactive XR applications.
• Students must be self-motivated, interested in thinking critically about 3D interaction, and willing to commit approximately 2-5 hours per week outside of class on assigned readings, virtual reality programming assignments, and project development. Familiarity with basic concepts of computer graphics and/or human-computer interaction and/or prior experience with the Unity game engine is beneficial.
• This class can be placed in the CSC GRE slot in the degree audit for Game Development Concentration students.

CSC 591/791 – 002, Advanced Real-Time Intelligent CPS

• Requisites: CSC 505 (recommended pre-requisite); CSC 501 or CSC 591/714 (co-requisite, required)
• Description: This course focuses on the integration of computing, communication, and control with physical processes and timing constraints, addressing challenges in areas such as connected vehicles, smart healthcare systems, wearable sensing, and robotics. Topics may include: Modeling and analysis of networked cyber-physical systems (CPS), Advanced real-time scheduling theory, System validation and certification, and Applications of AI, embedded systems, and control theory. Students typically conduct extensive literature surveys and/or implement software/comparative research-oriented projects as a major part of the grade.

CSC 591/791 – 003, Trustworthy AI for Software Engineering

• Pre-requisites: CSC 326. Python and Machine Learning basics recommended.
• Description: Basic and Advanced topics on Generative AI for Software Engineering

CSC 591/491 – 004, Control Systems for Robotics

• Pre-requisites: PY 208, MA 242, [MA 305 or MA 405]
• Description: Introduction to dynamics and control for robotic systems tailored for computer scientists. Concepts including ordinary differential equations, kinematics, and dynamics for common air and ground robotic systems will be introduced. Systems concepts such as step, impulse responses, Laplace Transform will be introduced. Feedback control via classical PID, and modern state-space and observer-based design will be explored. Emphasis on implementation, and simulation on an aerial multicopter robot will help students visualize and evaluate learning and control design performance.

CSC 591/491 – 005, Software Engineering and AI

• Pre-requisites: CSC 230.
• Description: AI presents unique challenges and opportunities when applied to software engineering. Unlike other domains, SE involves evolving requirements, human-in-the-loop decisions, and complex socio-technical ecosystems, making the integration of AI both powerful and precarious. This course will explore AI methods for SE, such as explainable AI, classification and clustering, multi-objective optimization, semi-supervised learning (useful when labeled data is scarce), theorem proving and logical reasoning, and generative methods enable code suggestion, test generation, and documentation support.

CSC 591/791 – 007, Deep Learning Beyond Accuracy

• Pre-requisites: Hands-on implementation experiences on deep neural network architectures.
• Description: In this course, students read and discuss research papers about deep neural networks with a focus on not just accuracy but also resource consideration e.g., FLOPs, parameter counts, time, memory, etc. With that interest, papers about techniques to design an efficient neural network architecture, such as structured/unstructured pruning, knowledge distillation, and quantization, will be read. On top of that, other dimensional metrics of machine learning, such as trustworthiness/robustness, fairness, or privacy, will also be explored. As a seminar course, this course is dedicated to paper reading, presentation, and discussion. As this is an advanced deep learning course, students are expected to have prior implementation experiences in deep neural network architectures. Students will conduct one term project and take no exam. Students are expected to have implementation experiences on (deep) neural networks, read/present/discuss ideas from research papers, and conduct a term project and submit a term paper.
• This class is also cross-listed with ECE.

CSC 591 – 008, Introduction to Computational Social Choice

• Requisites: a course that featured mathematical notation and reasoning
• Description: Computational social choice (CSC) is concerned with the design and analysis of mechanisms for collective decision making. The course provides an overview of major thrusts in this rapidly growing field including (but not limited to) voting, fair allocation, and coalition formation. Theoretical and computational concepts to be covered cut across a number of disciplines including Computer Science, Economics, Operations Research, and the Social Sciences. The presented concepts will be related to traditional and contemporary applications.

CSC 591/491 – 011, Animal Centered Computing

• Pre-requisites: CSC 316.
• Description: Decades of advances in human-computer interaction have produced well understood principles governing the processes, form, and function of computing systems that human users interact with on a daily basis. But what happens when users are nonhuman animals? How do we produce technology that enables nonhuman animals to interact with and through computers? How do we design these systems when users have drastically different physical and cognitive capabilities? The burgeoning field of Animal-Computer Interaction (ACI) is a highly multidisciplinary practice that seeks to answer exactly these types of questions. Advances in ACI draw upon ideas from ethics, interaction design, ergonomics, applied behavior analysis, Artificial Intelligence, analytics, electrical engineering and more. This special topics course will survey the history of technologies for nonhuman animals and the field of ACI. The course format will be seminar style with weekly readings of research papers and group discussions. Students will also conduct a semester-long project of their own design. There may be several field trips around the triangle area, so access to transportation is strongly encouraged.

CSC 591/791 – 022, Advanced NextG Network Design

• Pre-requisites: n/a.
• Description: This seminar course explores advanced modeling, simulation, and analysis techniques for modern computer and communication networks, with a strong emphasis on AI-driven and data-centric networking. The course begins by reinforcing foundational network concepts that underpin today’s Internet and wireless systems, and then builds upon them to introduce next-generation networks that leverage machine learning, optimization, and intelligent decision-making. Topics include predictive network analysis, performance modeling, protocol designs and simulation. A key feature of the course is its hands-on and exploratory nature. Students will develop and experiment with simulators to observe, analyze, and optimize network behavior under diverse topologies, workloads, and dynamics. By modifying and extending state-of-the-art simulation tools, students will gain practical experience in applying advanced techniques to understand complex network behaviors and design intelligent, future-ready networking systems.
• This class is also cross-listed with ECE.

CSC 591/714 – 023, Real Time Computer Systems

• Pre-requisites: CSC 501.
• Description: Design and implementation of computer systems required to provide specific response times. Structure of a real-time kernel, fixed and dynamic priority scheduling algorithms, rate monotonic scheduling theory, priority inheritance protocols, real-time benchmarks, case study of a real-time kernel.

CSC 591/791 – 024, Cognitive Systems

• Pre-requisites: CSC 411/520.
• Description: This course covers the study of intelligent systems and their behavior both natural and artificial. Topics will include the study of influential and state of the art publications related to perception (e.g., visual stimuli, language models, and non-verbal communication), learning and action (e.g., machine intelligence; computational creativity; common-sense reasoning), and the communication interface between different groups of intelligent agents (e.g., human-human, human-AI interaction, AI-AI).

CSC 591/791 – 027, Quantum Communications and Networking

• Pre-requisites: [MA 305/405], ST 370, CSC 401/570/573
• Description: This course covers the basic concepts of quantum communications and network (QCN). The objective of this course is to provide students with the theoretical foundation of QCN. The course is organized by lecturing, homework, and hour exams. As an introductory course on QCN, only minor assumptions are made in terms of students’ prerequisite knowledge.
• The tentative topics to be covered are quantum gates/operations, entanglement, teleportation, error control, purification, metrics for performance assessment, optical channel modeling, network graphs and quantum graph states, and quantum applications.
• This class is also cross-listed with ECE.

CSC 591/791 – 028, Human-centric IoT Systems

• Pre-requisites: Interest in human-centered design and interdisciplinary thinking
• Description: Human-centric IoT Systems is a graduate-level seminar that explores the design and analysis of Internet of Things systems with humans at the core. The course examines sensing technologies for capturing human and environmental data, signal processing methods for extracting meaningful information, and AI-driven decision making for intelligent and adaptive behavior. Emphasis is placed on integrating domain knowledge into system design to ensure usability, reliability, and societal relevance. The seminar also addresses system-level considerations, including architecture, scalability, and robustness, as well as security and privacy challenges in human-centric IoT and their mitigation strategies. Through lectures, paper discussions, and student-led presentations, participants gain a holistic understanding of how to build secure, intelligent, and human-aware IoT systems.
• This class is also cross-listed with ECE.

CSC 591/791 – 029, GenAI for Software Evolution and Maintenance

• Pre-requisites: CSC 326.
• Description: This course focus on the use of GenAI for several activities in the Software Evolution and Maintenance, such as program comprehension, documentation update, architecture redesign, change impact analysis and risk assessment, code smells identification and refactoring, bug fixing, adding new features, code translation, etc. The goal is to explore how different GenAI technologies (e.g., LLMs, Agents, Agentic Systems) can be applied to support (or replace) software engineers working with existing codebases and legacy systems. The content will research-oriented, which involves reading papers, proposing and conducing new studies, and describing and analyzing the results in a scientific report.

CSC 591/791 – 030, Advanced Robotics

• Pre-requisites: [MA 305/405] and ST 370. Machine learning and Python recommended.
• Description: This advanced robotics course covers robotics through computer science and AI, focusing on the autonomy loop with topics like ROS, perception, decision-making, and reinforcement learning in single- and multi-robot systems. Students will gain a strong theoretical foundation and practical experience by implementing and extending algorithms in simulations and real robotic platforms.
• This class is also cross-listed with ECE.

CSC 591/791 – 038, Machine Learning with Graphs

• Pre-requisites: In
• Description: Graph is a fundamental tool for modeling the relationships between objects in numerous real-world applications. Machine learning with graphs enables the modeling of attribute and relation information in graph data, and it presents significant prediction power in various promising domains. The key topics included in this course: introduction to graph theory, introduction to machine learning, spectral graph theory, spectral embedding, network embedding, link analysis, label propagation, graph neural networks (GNNs), robustness and scalability of GNNs, applications of GNN’s in specific domains (e.g., recommendation system, knowledge graph, computer vision, natural language processing, and social network analysis).
• This class is also cross-listed with ECE.

CSC 591/791 – 039, Mobile Health Systems and Applications

• Pre-requisites: a general background in machine learning and interest in cyber-physical systems/IoT.
• Description: The course introduces students to the growing field of mobile health, where low-cost wearable and mobile devices are used to collect daily-life data for discovering and monitoring digital biomarkers of health conditions. It covers the full pipeline: how different sensors acquire health-relevant data, how to process physiological and behavioral signals with advanced signal processing and machine learning, how to build and deploy mHealth systems in real-world settings, and how to evaluate their performance for diagnostics and disease progression detection. Privacy concerns in real-world deployment are also discussed. Topics include audio sensing, inertial measurement units (IMU), photoplethysmography (PPG), contactless radio sensing, brain signals, and applications such as Parkinson’s disease management and mental health.

CSC 791 – 001, Human-Computer Interaction

• Pre-requisites: n/a
• Description: Basic theory and concepts of human-computer interaction. Human and computational aspects. Cognitive engineering. Practical HCI skills. Significant historical case studies. Current technology and future directions in user interface development.
• This class is also cross-listed with CSC 554 – 001.

CSC 791 – 005, TA Training for PhD Students

• Co-requisite: CSC 801.
• Description: The course will have three main components:
  • use of tools and technologies for course management, such course website, grading tools and discussion forums
  • student interactions and ethics related to grading, office hours and discussions
  • weekly sessions in which students share experiences and receive feedback from the instructor and their peers

CSC 791 – 023, Geospatial AI

• Pre-requisites: email instructor.
• Description: email instructor.
• This class is also cross-listed with GIS.

ECE-taught classes:

Information on classes that are taught by the ECE department can be found here: https://my.ece.ncsu.edu/undergrad/academic-information/special-topics/:

• CSC 591 – 006: Advanced Microarchitecture
• CSC 591 – 081: Advanced Efficient Deep Learning
• CSC 591 – 082: Open RAN: Foundations & App
• CSC 791 – 004: Information Theory

All special topics are offered for 3 credit-hours and must be taken for a letter grade. Students are limited to 12 hours of Special Topics in CSC. Graduate classes may not be changed to S/U (credit-only). Undergraduate students who are in the ABM or CSC Honors may use the Graduate Request form to request enrollment.

CSC 591/791 – 001, Software/Hardware Co-Design for Intelligent Systems

• Pre-requisites: Intro Programming (C++ or Python), MA 305, and ST 370
  • Recommended: prior knowledge of computer hardware
• Description: Fueled by the proliferation of sensing data and the advancement of machine learning (ML), intelligence is becoming a household brand in many Cyber-Physical Systems and Internet-of-Things applications. This course will offer in-depth coverage of the latest software-hardware co-design methodologies for developing energy-efficient, low-latency, reliable and trustworthy intelligent systems. Techniques that are widely investigated and adopted in industry companies and academic communities will be discussed and practiced. Topics include but are not limited to: ML basics and system performance evaluation metrics, hardware platforms and software frameworks, modern neural networks (e.g. CNN, LLM), training and testing, design paradigms of algorithm-hardware co-design for ML acceleration like hardware-oriented model compression, pruning and quantization, sparsity, modern ML hardware architectures, near memory and in memory computing, trustworthy and private computing by design, machine vision guided image compression etc. Hands-on projects will be utilized with real-world applications and datasets. The final project is expected to be deployed and evaluated on hardware platforms (such as embedded GPU, embedded TPU) when possible. Classes will be running by combining lecture sessions, presentation sessions, and labs/project.

CSC 591 – 002, Foundations of Data Science

• Pre-requisites: MA 305, and ST 370
• Description: Students will learn core data science principles related to computational statistical data analysis. This course introduces ideas in statistical learning and will help students prepare for advanced data mining and machine learning courses. Focus will also be given to applying these principles for a variety of data analysis tasks using R and Python. Topics include random variables and probability distributions, exploratory data analysis, variable selection, sampling methods, histograms, and probability distributions, density estimation, hypothesis testing, missing data and imputation, mixture models, latent variables, and expectation maximization, regression analysis, discriminant analysis, bagging and boosting, principle component analysis, information theory — entropy, mutual information, Bayesian information criteria, conditional independence, re-scaling and low-dimensional summaries, factor analysis, graphical causal models and causal inference, and evaluating predictive models.

CSC 408/591 – 003, Software Product Management

• Pre-requisite: CSC 326 (required), CSC 510 (recommended)
  • Description: This course focuses on advanced software product management from the software engineering perspective, as well as how those principles apply to management at the individual, team, and organizational levels. Topics include challenges in product management, road mapping, understanding and asserting business value, Objectives and Key Results (OKRs), risk management and task prioritization. This course includes substantial reading expectations and in-class exercises. Active and consistent participation throughout the semester is required. Multiple presentations will be required.

CSC 591/791 – 004, Cyber-Physical Systems Control

• Pre-requisite: CSC 316
• Description: This course introduces students to the research, design, and analysis of control and autonomy of cyber-physical robotic systems. Applications for CPS research are far reaching and span medical devices, smart buildings, vehicle systems, and mobile computing. The course will be conducted as a seminar course with weekly readings, presentations, and paper summaries required. A semester project allows students to creatively apply cutting-edge CPS research to their research problems. Helpful background includes digital control, real-time systems theory, automata theory, and optimization but is not required. Students from Computer Science, Computer Engineering, Electrical Engineering, and Mechanical Engineering should be appropriately prepared for this course.

CSC 486/591 – 006, Computational Visual Narrative

• Pre-requisite: CSC 316
• Description: Computational Visual Narrative is a project-based course for developing computational media with visual computing tools such as game engines. Within this course we will gain familiarity with the repertoire and practice of individuals involved in the design and development of digital interactive experiences. In the process, you will gain an understanding of the underlying concepts, techniques and technologies of computational and digital systems, software development and its role and potential in narrative practice.

CSC 591/791 – 006, Efficient Deep Learning

• Pre-requisites: MA 305 and ST 370 (required); Python programming, CSC 411/520, and CSC 422/522 (recommended)
• Description: This is a research-oriented advanced course focused on the latest frontiers of efficient deep learning. It introduces key techniques such as model compression, efficient training, neural architecture search, knowledge distillation, few/zero-shot learning, and distributed machine learning, with applications across natural language processing, computer vision, and beyond. Students will explore these topics through paper discussions, research projects, and presentations, gaining both theoretical foundations and practical experience in designing resource-efficient AI systems.

CSC 491/591 – 007, Cyber-Physical Systems for Biometrics

• Pre-requisite: CSC 316
• Description: Given the increasing advances of mobile and wearable technologies in people’s daily life, many biometrics and authentication techniques are being explored and developed (e.g., fingerprint lock on smartphones). This course introduces students to the theory and practice of biometric (e.g., face and voice) authentication systems. The course will cover the fundamental principles of biometrics, including physiological and behavioral characteristics (what & why), AI & Machine Learning approaches used to extract, represent, and match these characteristics (how), and system architecture (integration). Students will also learn about the newest approaches to biometrics and how they fit in its technological landscape, as well as the ethical and legal considerations surrounding the use of biometrics. The course will contain a project where students will group together to implement an authentication system prototype.

CSC 591/791 – 008, Deep Learning Beyond Accuracy

• Pre-requisite: CSC 316. Students are expected to have implementation experiences on (deep) neural networks.
• Description: In this course, students read and discuss research papers about deep neural networks with a focus on not just accuracy but also resource consideration e.g., FLOPs, parameter counts, time, memory, etc. With that interest, papers about techniques to design an efficient neural network architecture, such as structured/unstructured pruning, knowledge distillation, and quantization, will be read. On top of that, other dimensional metrics of machine learning, such as trustworthiness/robustness, fairness, or privacy, will also be explored. As a seminar course, it is dedicated to paper reading, presentation, and discussion. Students will conduct one term project and take no exam.

CSC 591 – 009, Applied AI to Industry

Pre-requisite: CSC 411/520 and CSC 422/522

Description: The objective of this course is to provide students with knowledge on essential aspects required to successfully implement Artificial Intelligence in industry. In addition to the theoretical concepts discussed, the instructor, who has over 30 years of experience applying AI in industry, will discuss how this theory applies to solve industry problems.

CSC 491/591 – 010, Software Engineering for Robotics Today

• Pre-requisite: CSC 326
• Description: This class is offered because the complex interplay between software and hardware is increasingly in control of our tools, environments, and lives—and there are daunting challenges. We should learn the best architectures, tools, and processes and learn to identify the hardest problems in this design domain and what can be done to address these challenges.

CSC 491/591 – 011, Human Centered Security

• Pre-requisite: CSC 316
• Description: Human-Centered Security is a computer security course for graduate and advanced undergraduate students introducing the concepts and methods of human-centered cyber security research. Topics include the design, planning, execution, and statistical analysis of research studies. Some programming skill are recommended for both the topic area of usable security for developers (because this will involve programming concepts like libraries and commits) and when writing data analysis and visualization scripts. CSC 474 will be helpful with understanding the research context.

CSC 491/591 – 012, Ubiquitous Computing and Mobile Health

• Pre-requisite: CSC 316
• Description: This course introduces how wearable and mobile systems sensors can be used to gather data relevant to understand health, how the data can be analyzed with advanced signal processing and machine learning, and the evaluation performance of these systems in terms of diagnostics and disease progression detection. The course will also touch on how to solve privacy concerns in building mobile health systems in the real world.

CSC 491/591 – 013, Software Engineering and AI

• Pre-requisite: CSC 316
• Description: AI presents unique challenges and opportunities when applied to software engineering. Unlike other domains, SE involves evolving requirements, human-in-the-loop decisions, and complex socio-technical ecosystems, making the integration of AI both powerful and precarious. This course will explore AI methods for SE, such as explainable AI, classification and clustering, multi-objective optimization, semi-supervised learning (useful when labeled data is scarce), theorem proving and logical reasoning, and generative methods enable code suggestion, test generation, and documentation support. https://github.com/txt/se2626/blob/main/README.md

CSC 491/591 – 014, How to be a Software Guru

• Pre-requisite: CSC 316
• Description: Classes teach you all about advanced topics within CS, from operating systems to machine learning, but there’s one missed subject: proficiency with their tools. Our motto: “be quiet or I will replace you with one tiny shell script”.

CSC 591 – 015, AI of Things

• Pre-requisite: CSC 316 (required), Python (recommended)
• Description: This course investigates the use of Artificial Intelligence (AI) in Internet of Things (IoT) applications. An introduction to AI will be provided, together with a set of IoT applications that can benefit from AI. Programming projects will be conducted in Python.

CSC 591 – 016, Algorithms on Strings

• Pre-requisites: CSC 505
• Description: This course is a gentle introduction to string algorithms and related data structures from a theoretical point of view. The topics can be roughly categorized into four modules as follows. The first module consists of fundamental string-matching algorithms like the Karp-Rabin algorithm, the Knuth-Morris-Pratt algorithm, and the Aho-Corasick algorithm. We will also see more complex matching problems under hamming distance and their efficient solutions using the Fast Fourier Transform (FFT). The second module will be on basic data structures such as the suffix trees, suffix arrays, and longest common prefix (LCP) array. Their linear time construction algorithms and applications in solving problems such as the longest/average common substring, shortest unique substring, maximal exact/unique matches, suffix/prefix overlap, Lempel-Ziv parsing, etc., will also be covered. The third module will be on succinct data structures and their applications in compressed text indexing. Sub-topics are rank/select queries on bit vectors and sequences, Wavelet trees, range minimum queries, Compressed Suffix Arrays, Burrows-Wheeler Transform and FM-index, compressed LCP array, etc. In the fourth module, we will see some NP-hard problems, such as the median/center string problem, shortest superstring problem, etc., and efficient approximation algorithms for solving them.

CSC 591 – 017, Self-Driving Cars

• Pre-requisite: CSC 316 (required), MA 305/405, ST 370, Python (recommended)
• Description: This course explores the theory and practice of building self-driving cars using advanced computing technologies. It aims to provide students with opportunities to i) understand the introductory theory that enables autonomous driving and ii) gain extensive hands-on experience with various software and hardware tools. Topics include robotics software programming, sensor fusion, control theory, and introductory perception, planning, and navigation techniques using machine learning and computer vision. Over the course of the semester, students work in small groups to design and build software systems for miniaturized self-driving cars that autonomously navigate an indoor track resembling real road environments. Students demonstrate their learned skills through the final driving showcase.

CSC 791 – 002, Intelligent Tutoring Systems for CS Education

• Pre-requisite: CSC 522 or other ML/DS course
• Description: This seminar explores the use of intelligent tutoring systems (ITS) and other adaptive learning technologies in computer science education. As adaptive learning technologies revolutionize fields like math and physics, they also present unique challenges when applied to computer science. Topics covered in this course include knowledge representation, student modeling, and the delivery of adaptive support tailored to individual learners. Students will engage with key concepts such as student modeling, adaptive scaffolding, and personalized recommendations within the context of CS education. Through a combination of reading state-of-the-art research, class discussions, and a semester-long project, students will gain hands-on experience in developing and applying adaptive learning technologies to improve the teaching and learning of computer science.

CSC 791 – 003, Multimodal data analysis

• Pre-requisite: Python, MA 305, and ST 370
• Description: This advanced course on multimodal data analysis will explore how meaning is made across data that spans multiple modalities such as language, visuals, sound, gesture, and digital media. Students will engage with leading theories and hands-on methods for analyzing complex, real-world data from but not limited to social media, videos, scientific images and interactive platforms, in a semester-long project. The course will discuss foundational concepts and cutting-edge methods relating to multimodal analysis, and challenges relating to representation, alignment, translation, fusion, co-learning and ethics. In addition to these, concepts relating to visualizations and interpretation will be discussed through instructor-led lectures (discussion of case-studies) and research papers on topics related to and advancing on the fundamental concepts of multimodal data analysis presented by students.

CSC 791 – 005, Seminar on Knowledge Graphs and Neurosymbolic AI

• Pre-requisite: CSC 540 and [CSC 520 or 522]
• Description: This graduate seminar explores the emerging intersection of Knowledge Graphs (KGs) and Neurosymbolic AI (NSAI), a subfield that aims to combine the structured reasoning capabilities of symbolic AI with the pattern recognition and learning strengths of neural networks. The course is designed for Ph.D. students (and thesis MS students with approval from instructor) who are pursuing research-oriented projects in artificial intelligence, data science, or knowledge representation.
• The course begins with foundational lectures on constructing and managing knowledge graphs, covering ontologies, semantic web standards, and graph query languages. It then explores how knowledge graphs can support symbolic reasoning within neural models, introducing key neurosymbolic AI paradigms such as Logic Tensor Networks, semantic loss functions, and differentiable logic frameworks.

Proposed topic listing:

• TBD (Dr. Raghvendra)
• TBD (Dr. Thankachan)
• TBD (Dr. B. Xu)
• Self-driving cars: Theory and Practice
• Foundations of Data Science
• Artificial Intelligence of Things
• CPS for Bio metrics
• Efficient Deep Learning
• Multimodal data analysis
• Software for Gurus
• Systems for neuro-symbolic AI
• Ubiquitous comp. & mobile health

ECE-taught classes:

Information on classes that are taught by the ECE department can be found here: https://my.ece.ncsu.edu/undergrad/academic-information/special-topics/: