Artificial Intelligence (AI)

AI 100. Introduction to Artificial Intelligence

Credits: 3
Typically Offered: FALL
In this course, students learn basic concepts and applications of artificial intelligence (AI), including AI project cycles. There will be a focus on AI issues, including ethics, bias, culture, regulation, and professional expectations.

AI 110. Introduction to Machine Learning

Credits: 3
Prerequisites: AI 100, CIS 185, and MATH 210 with a C grade or higher.
Typically Offered: FALLSPR
This course introduces machine learning concepts and Python applications, including data acquisition, supervised and unsupervised learning, and data modeling.

AI 210. Introduction to Natural Language Processing

Credits: 3
Prerequisite: AI 110 with a C grade or higher.
Typically Offered: FALL
This course provides fundamental concepts of Natural Language Processing (NLP) and text processing, focusing on the knowledge and skills required to create language recognition applications.

AI 220. Introduction to Computer Vision

Credits: 3
Prerequisite: AI 110 with a C grade or higher.
Typically Offered: SPRING
This course provides fundamental concepts in Computer Vision, focusing on the knowledge and skills necessary to design problem-solving applications.

AI 230. Artificial Intelligence for Business Solutions

Credits: 3
Prerequisite: AI 210 and AI 220 with a C grade or higher.
Typically Offered: FALL
This course uses artificial intelligence (AI) and machine learning (ML) concepts to create business solutions.

AI 240. Artificial Intelligence Capstone Project

Credits: 3
Prerequisite: AI 210 and AI 220 with a C grade or higher.
Typically Offered: SPRING
Students will continue application of artificial intelligence (AI) and machine learning (ML) through development of these technologies to address various business challenges.

AI 300. Artificial Intelligence

Credits: 3
Prerequisites: AI 110 and CIS 185.
Typically Offered: FALL
In this course, students will continue the study of artificial intelligence concepts and applications including evaluating suitability for AI tools in development projects.

AI 310. Machine Learning

Credits: 3
Prerequisites: AI 110 and CIS 185.
Typically Offered: SPRING
This course continues the study of Python applications for supervised learning and data modeling.

AI 340. IML Capstone Project I

Credits: 1
Corequisite: AI 360.
Typically Offered: SPRING
In this course students will apply their knowledge of AI to solve a real world problem either of their own choosing or from an industry partner. This course either continues AI 240 or starts a new project.

AI 360. Computational Methods for AI & ML

Credits: 3
Prerequisites: AI 110, CIS 185 and CIS 204.
Typically Offered: SPRING
This course presents the fundamental mathematics and computational methods in artificial intelligence and machine learning.

AI 410. Natural Language Processing

Credits: 3
Prerequisites: AI 210, AI 310 and AI 360.
Typically Offered: FALL
This course provides a deeper exploration of Natural Language Processing, focusing on current models and training.

AI 420. Computer Vision

Credits: 3
Prerequisites: AI 220, AI 310 and AI 360.
Typically Offered: FALL
This course provides a deeper exploration of Computer Vision, focusing on current models and applications.

AI 440. IML Capstone Project II

Credits: 1
Prerequisite: AI 340.
Corequisites: AI 410, AI 420 and AI 450.
Typically Offered: SPRING
In this course students will apply their knowledge of AI to solve a real world problem either of their own choosing or from an industry partner. This course continues AI 340.

AI 450. Deep Learning

Credits: 3
Prerequisites: AI 310 and AI 360.
Typically Offered: FALL
This course provides an exploration of the concepts and applications of Deep Learning. Emphasis is placed on neural network development, and successful project leadership.

AI 460. AI Ethics

Credits: 3
Prerequisite: AI 100.
Typically Offered: SPRING
In this course, students will describe the reasons for ethical analysis applied to AI, critically analyze current AI policies, and apply ethical and socially responsible principles for AI use in professional life.

AI 470. Generative AI

Credits: 3
Prerequisite: AI 100.
Typically Offered: SPRING
In this course students will explain the fundamental concepts and capabilities of generative AI, discuss the limitations of AI models, and apply techniques to write effective prompts and generate desired outcomes desired for AI models. Ethical analysis of generative AI use is discussed.