Artificial Intelligence bs

• DATA 201 - Introduction to Data Science
  • This course provides an introduction to data science. Topics include data collection, processing, analysis and visualization. Additional topics include clustering algorithms and regression. Students will learn how to critically evaluate and produce their own quantitative results. This is a projects based course.
• DATA 301 - Data Science Methodology
  • This course introduces modern statistical and machine learning techniques and demonstrates their application on real world datasets. Topics include advanced clustering algorithms, tree-based data analysis including random forest and gradient boosted trees and neural network-based systems. Students will use these algorithms to solve real world problems. This is a projects-based course.
• CPSC 336 - Network Implementation and Administration I
  • Study of Linux-based network and cloud systems, with emphasis on command-line administration, TCP/IP networking, secure remote access, server configuration, virtualization, cloud compute, storage, and database services. Students configure and administer systems such as web servers, database servers, virtual machines, and cloud instances, with attention to functionality, reliability, and security.
• CPSC 401 - Neural Foundations of Computer Vision
  • This course introduces the neural network foundations used in modern computer vision systems. Students study methods for training deep neural networks, including gradient-based optimization and representation learning for visual data. Topics include convolutional architectures for image analysis, transfer learning with pre-trained models, and techniques for object detection and image segmentation. Students also learn evaluation metrics used to assess vision systems. Programming assignments emphasize building, training, and evaluating neural network models for practical visual recognition tasks.
• CPSC 403 - Large Language Models & Agentic Systems
  • This course prepares students to understand and deploy modern language model systems. Students examine transformer architectures and the mathematics of attention before working with contemporary development workflows in the Hugging Face ecosystem. Emphasis is placed on adapting large models under practical hardware constraints using parameter-efficient fine-tuning methods such as LoRA and QLoRA. Students also construct retrieval-augmented generation pipelines that combine language models with embeddings, semantic search, and vector databases. The course concludes with agentic AI systems in which models interact with external tools and environments through structured workflows to perform complex, multi-step tasks.
• CPSC 497 - Capstone Project in Artificial Intelligence
  • In this course, you will apply machine learning, data engineering, and algorithmic foundations to propose, architect, and deploy a semester-long artificial intelligence project. The project requires a computationally significant effort, integrating data pipeline construction, model selection and optimization (e.g., hyperparameter tuning or fine-tuning), and rigorous performance validation. The course follows a structured lifecycle, beginning with selecting and defending a technical project proposal. Once approved, weekly status updates are expected alongside two intermediate presentations. This process culminates in a final project submission, defense, and poster presentation.

Select two from:

• CPSC 441 - Big Data Technologies
  • Covers facets of cloud computing and big data management, including the study of the architecture of the cloud computing model with respect to virtualization, multitenancy, privacy, security, cloud data management and indexing, scheming and cost analysis; also includes relevant programming models, crowdsourcing and data provenance.
• CPSC 472 - Introduction to Robotics
  • An overview of applied robotics. Covers introductions to configuration space representations, rigid body transforms in 2D and 3D, robot kinematics, basic control theory, motion planning, perception, and machine decision making. Perception topics include basic computer vision and laser rangefinder (LIDAR)-based obstacle detection and mapping. Hands-on development and system integration using various robotic platforms. Programming is done in Linux in a mixture of C++ and Python; no prior experience is required.
• CYBR 484 - AI Applications in Cybersecurity
  • Examines the use of artificial intelligence to analyze and defend modern computing systems. Students learn to treat network traffic, system logs, and executable files as datasets that can be analyzed with statistical and machine learning methods. Using Python, students build tools for parsing security data, detecting anomalous network activity, classifying malware, and identifying phishing attempts. The course also introduces techniques for securing AI-enabled systems, including defenses against prompt injection and related attacks on automated decision systems. Emphasis is placed on practical workflows for threat detection, investigation, and risk prioritization within complex computing environments.
• CPSC 510 -  Artificial Intelligence I
  • An introduction to the mathematical and computational foundations of artificial intelligence, with emphasis on the elements most useful for practical applications. Topics include heuristic search, problem solving, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, expert systems, machine learning and language understanding, including Markov decision processes, reinforcement learning, hidden Markov models, Bayes nets and Naïve Bayes. Programming assignments are required.
• PHYS 441 - Modeling and Simulation
  • The modeling and simulation of physical systems. Applying software methodologies to the solution of physical problems. Lectures typically involve a short review of a physics topic, such as Keplerian motion, followed by an extensive discussion of the modeling and simulation of the problem.
• PHYS 541 - Modeling and Simulation
  • Graduate-level study of the modeling and simulation of physical systems. See the CNU Graduate Catalog for the complete course description and prerequisites.
• MATH 380 - Numerical Analysis I
  • A survey of numerical methods for scientific and engineering problems. Topics include root-finding techniques, polynomial and spline interpolation, numerical differentiation and integration, and the numerical solution of initial value problems for ordinary differential equations. Consideration is given to theoretical concepts and to efficient computation procedures. Computer projects are required.
• other approved artificial intelligence elective