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• 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
• CPSC 472 - Introduction to Robotics
• CYBR 484 - AI Applications in Cybersecurity
• CPSC 510 -  Artificial Intelligence I
• PHYS 441/541 - Modeling and Simulation
• MATH 380 - Numerical Analysis I
• other approved artificial intelligence elective