Artificial Intelligence and Machine Learning (AIML)

AIML 1860  INTRODUCTION TO GENERATIVE AI (3 credits)

Generative AI is a form of machine learning that can generate content. AI Image Generators like Midjourney are capable of creating images nearly indistinguishable from human artwork. Large Language Models (LLMs) like ChatGPT have advanced abilities concerning understanding human language. LLMs can carry on conversations, write essays and stories, and even generate code. Generative AI is the defining technology of our times. This course provides an introduction to this technology: How it works, and how to use it.

Prerequisite(s): Students must have met the Quantitative Literacy and Data Literacy requirements as defined by Mav Ed.

AIML 2060  CONCEPTS OF ARTIFICIAL INTELLIGENCE (3 credits)

The course will introduce students to the foundational concepts and elements of techniques in Artificial Intelligence (AI), including representation, heuristic search, automated problem-solving, decision-making, and machine learning. Students will study the history of AI and the lessons learned from it, as well as a range of real-world applications in which AI is currently used. Assignments will enable students to get a feel for AI techniques. This course will be the first contact with AI concepts in the AIML program and it will provide a broad overview of the areas of AI. It will be open for CS students as an elective.

Prerequisite(s): CSCI 1620 (with a grade of C or better) and MATH 1950 (with a grade of C or better); Co-requisite: CSCI 2030

AIML 2470  CONCEPTS OF MACHINE LEARNING (3 credits)

This course introduces the practical concepts and contemporary applications of machine learning. It offers a broad, application-driven entry point designed to help learners apply and interpret machine learning methods without requiring deep mathematical preparation. Topics span supervised, unsupervised, generative, and reinforcement learning, emphasizing hands-on experience and model evaluation. Core machine learning architectures, such as neural networks and Transformers, are presented alongside milestone models that have shaped machine learning advances, such as generative and large-scale systems. Through structured assignments and a final project, participants will gain hands-on experience with widely used frameworks, critically assess model performance, and develop a practical understanding of both the potential and the limits of current AI technologies.

Prerequisite(s): CIST 2500 with a grade of C- or better and CIST 1400 with a grade of C or better; or Instructor Permission.

AIML 2860  VIBE CODING (3 credits)

Large Language Models (LLMs) are revolutionizing how software is developed by shifting the focus from mastering syntactic details of programming languages to crafting high-level specifications and robust validation/testing strategies. While this new paradigm, called Vibe Coding, simplifies certain aspects of development, a foundational understanding of programming languages and systems remains essential. This course embraces an LLM-centric approach to software development. This involves prompt engineering, interpreting AI-generated code, and iteratively debugging and refining software solutions.

Prerequisite(s): AIML 1860 and (PHIL 2010 or CSCI 2030). Not open to non-degree graduate students.

AIML 4970  ARTIFICIAL INTELLIGENCE CAPSTONE PROJECT (3 credits)

The Capstone Project completes the undergraduate experience of an Artificial Intelligence major. Students will develop a real-world AI-based system or conduct supervised research in an area of AI, applying fundamental artificial intelligence concepts, practices and principles as required by the problem.

Prerequisite(s): CSCI 4450 with C- or better and senior standing in Artificial Intelligence program. Not open to non-degree graduate students.

AIML 4980  SPECIAL TOPICS IN ARTIFICIAL INTELLIGENCE (3 credits)

This is a variable topic course in artificial intelligence at the senior/graduate level. Topics not normally covered in the artificial intelligence degree program, but suitable for senior/graduate-level students can be offered.

Prerequisite(s): Permission of instructor. Additional prerequisites may be required for particular topic offerings.

AIML 4990  INDEPENDENT STUDIES IN ARTIFICIAL INTELLIGENCE (1-3 credits)

This is a variable credit course for the junior or senior who will benefit from independent reading assignments and research type problems. Independent study makes available courses of study not available in scheduled course offerings. The student wishing to take an independent study course should find a faculty member willing to supervise the course and then submit, for approval, a written proposal (including amount of credit) to the Artificial Intelligence Undergraduate Program Committee at least three weeks prior to registration.

Prerequisite(s): Written permission required. Independent study proposals must be approved by the AI Undergraduate Program Committee.