AI Science B.S.

AI core courses – 15 credits

ASE 309
Technical Fundamentals of AI
Intelligent agents, knowledge representation and reasoning, machine learning, neural networks (dense, convolutional, and transformer-based), reinforcement learning, and large language models. Students build a simple expert system, train an ML model, and develop an LLM-based application.
Credits: 3
Year: 2

ASE 310
AI Experiential Programming
Develop AI with AI — students use LLM-powered programming tools the same way practicing engineers use them. Topics include evaluating LLM-generated code, building RAG applications, and developing AI agents via LangChain and MCP frameworks.
Credits: 3
Year: 2

ASE 465
Introduction to Machine Learning
Feature extraction; supervised and unsupervised learning; bias-variance tradeoff; linear, logistic, and nonlinear regression; decision trees and ensemble models; neural networks; and deep learning. Prerequisite for ASE 469.
Credits: 3
Year: 2

ASE 469
Artificial Intelligence Algorithms
Core AI algorithms including tree search and constraint satisfaction, automated reasoning, probabilistic models and Monte Carlo methods, reinforcement learning, and automatic differentiation. Students build a game-playing AI system and implement deep RL for motor control.
Credits: 3
Year: 3

CIS 468
Natural Language Processing
Statistical and neural approaches to NLP — the language side of modern AI systems.
Credits: 3
Year: 3

Senior capstone sequence – 8 credits

A three-course project sequence bridging coursework and career. Students scope, build, and deliver a significant AI project — software or hardware — often in partnership with regional employers including Lockheed Martin, Saab, and Hidden Level.

ASE 453
AI Capstone 1
Scope, frame, and begin building a significant AI project with real-world stakes, often in collaboration with industry partners.
Credits: 3
Year: 4, Fall

CIS 454
AI Capstone 2
Develop, evaluate, and deliver portfolio-ready work. Students present results to faculty, peers, and external collaborators.
Credits: 3
Year: 4, Spring

ASE 491
AI Seminar
A companion seminar covering current research, industry speakers, and the professional landscape of AI — run concurrently with the capstone sequence.
Credits: 2
Year: 4, Fall
 

Concentrations – choose one (9 credits each)

Students declare their concentration in Fall of Year 3.

Software Concentration

Algorithms, data, intelligence

ASE 463 Data Mining
KDD process, supervised and unsupervised methods, recommendation systems, and scalability at massive scale.

ELE 453  Image and Video Processing
Foundations of computer vision — the perception side of AI.

CIS 473  Automata and Complexity
Theoretical grounding for the algorithms that power AI systems.

Hardware Concentration

Architecture, acceleration, silicon 

ASE 460  AI Hardware Design Fundamentals
Hardware foundations of ML; FPGA acceleration; model optimization for silicon; pipelining and systolic array architectures for DNN acceleration.

CSE 381  Computer Architecture
How CPUs and GPUs are built — the machines AI runs on.

CSE 384  Systems and Network Programming
Low-level systems programming for the hardware/software interface.