University of North Carolina at Charlotte · Courses
ITAI
14 courses with the subject ITAI, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
ITAI 1600Artificial Intelligence Literacy for Professional Success3
(3) Prerequisite(s): Major in Artificial Intelligence. AI literacy is a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool for school, at home, and in the workplace. The following questions frame the learning objectives of this course: What is AI?; How can I effectively interact with Generative AI?; What is the responsible use of AI? What are the broader social implications of AI? Guest speakers, including industry partners, discuss and explain aspects of a professional career in the field of AI and provide an overview of AI based on historical and current developments. Students will engage with case studies of the use of AI to support learning, ethical and responsible issues in the use of AI, and develop critical reviews of responses from generative AI such as the GPT models. Assignments include critical reviews of reading materials and the use of a generative AI tool.
ITAI 4153Introduction to Machine Learning Operations3
Prerequisite(s): ITSC 2214 with grade of C or above. Pre- or Co- requisite(s): ITCS 3156. A comprehensive introduction to the key components of the machine learning (ML) project lifecycle, integrating essential practices from machine learning, DevOps, and data engineering. Through hands-on learning, students will gain practical experience in deploying and maintaining ML models in production environments reliably and efficiently.
Prerequisite(s): ITSC 3155 and (ITAI 2153 OR ITCS 3156). This project- oriented course explores the integration of Artificial Intelligence (AI) with software development, covering multiple phases of the Software Development Lifecycle (SDLC), including planning, designing, coding, debugging, refactoring, testing, deployment, and management. A key focus is on how Large Language Models (LLMs) and Generative AI are reshaping development practices, especially considering trust and trustworthiness properties (encompassing security, reliability, privacy, and maintainability). Through an hands-on project, students will critically examine both the potential and risks of LLMs, such as biases and inaccuracies, in enhancing software trustworthiness. By working on a real-world (or realistic) application, students will apply AI-driven development tools, assess trustworthiness, and implement strategies to mitigate risks. This course emphasizes practical experience, enabling students to responsibly leverage LLM-based tools to build trustworthy software systems that meet industry standards.
ITAI 4155Artificial Intelligence Development Projects3
Prerequisite(s): ITAI 2153 and ITSC 3155 and ITCS 3156; Senior standing or permission of department. Students will apply artificial intelligence (AI) techniques to real-world problems through a capstone project experience. Students will explore the complete project development lifecycle, from problem identification and requirement analysis to solution design, implementation, testing, deployment, and evaluation. Emphasis will be placed on leveraging AI methodologies such as machine learning, deep learning, natural language processing, computer vision, and generative AI techniques. Students will work in teams to develop AI- driven software prototypes.
ITAI 4160Databases and Cloud for Artificial Intelligence3
Prerequisite(s): ITSC 1213 (C or Above), ITSC 2181 (C or Above). Explores the intersection of data management, cloud computing, and artificial intelligence, focusing on structured and unstructured data storage, processing, and analytics in scalable cloud environments for AI applications. Topics include relational and NoSQL databases, data modeling, statistical analysis, data wrangling, and big data processing. Students will gain hands-on experience with a cloud platform to build data pipelines and automate AI workflows and develop practical skills in managing and analyzing large-scale data to support AI and machine learning applications.
(3) Prerequisite(s): ITCS 5356 OR ITCS 6150 OR ITCS 5153 OR ITCS 5154. Cross-listed Course(s): ITAI 4154.This project-oriented course explores the integration of Artificial Intelligence (AI) with software development, covering multiple phases of the Software Development Lifecycle (SDLC), including planning, designing, coding, debugging, refactoring, testing, deployment, and management. A key focus is on how Large Language Models (LLMs) and Generative AI are reshaping development practices, especially considering trust and trustworthiness properties (encompassing security, reliability, privacy, and maintainability). Through an hands- on project, students will critically examine both the potential and risks of LLMs, such as biases and inaccuracies, in enhancing software trustworthiness. By working on a real-world (or realistic) application, students will apply AI-driven development tools, assess trustworthiness, and implement strategies to mitigate risks. This course emphasizes practical experience, enabling students to responsibly leverage LLM-based tools to build trustworthy software systems that meet industry standards.
ITAI 5160Databases and Cloud for Artificial Intelligence3
(3) Cross-listed Course(s): ITAI 4160. Explores the intersection of data management, cloud computing, and artificial intelligence, focusing on structured and unstructured data storage, processing, and analytics in scalable cloud environments for AI applications. Topics include relational and NoSQL databases, data modeling, statistical analysis, data wrangling, and big data processing. Students will gain hands-on experience with a cloud platform to build data pipelines and automate AI workflows and develop practical skills in managing and
ITAI 6880Individual Study. (1 to 3) Prerequisite(s)
Permission of department. With the direction of a faculty member, students plan and implement appropriate objectives and learning activities to develop specific areas of expertise through research, reading, and individual projects. May be repeated for credit with change of topic.
ITAI 6991Artificial Intelligence Thesis. (1 to 3) Graduate
thesis research. Detailed exploration of an area of computer science chosen for thesis research. May be repeated for credit but no more than six hours may be applied to M.S. degree requirements.
Subject
ITAI
Type
course
Repeatable
May be repeated for credit but no more than six hours may be applied to M.
