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2024-2025 Courses and Programs Relating to Artificial Intelligence

Academic Programs

Miami University is proud to announce the development of two new program focuses pointedly on artificial intelligence, which we hope to launch in fall 2025:

Deep Learning & Generative AI Graduate Certificate

This online Graduate Certificate in Generative AI focuses on specialized deep learning and generative AI skills. Designed for working professionals and recent graduates, this online program meets the growing industry demand for advanced AI expertise. It is also designed to be accessible to professionals from adjacent disciplines such as robotics and stats/data science.

Machine Learning & Artificial Intelligence Undergraduate Minor

This minor provides a cohesive program in the fundamentals of software development for artificial intelligence (AI) and machine learning (ML) applications, including Deep Learning and Generative AI concepts. The minor is not open to students majoring in computer science or software engineering.

Other programs that relate to the theme:

  • Advanced Business Analytics Certificate
  • Business Analytics, B.S. Business
  • Business Analytics, M.S.
  • Computer Engineering, B.S. Engineering
  • Computer Science, B.A. & B.S. in Engineering
  • Computer Science, M.S. and M.
  • Cybersecurity Administration Minor
  • Cybersecurity, B.S.
  • Cybersecurity & Networking, B.S. in Information Technology
  • Emerging Technology, Business + Design, B.A.
  • Emerging Technology, Business + Design Minor
  • Entrepreneurship & Emerging Technology, Certificate
  • Entrepreneurship & Emerging Technology, M.
  • Information Systems & Cybersecurity Management, B.S. Business
  • Robotics Engineering, B.S. in Engineering

Courses

CSE 432/CSE 532. Machine Learning. (3)

This course introduces the process, methods, and computing tools fundamental to machine learning. Students will work on large real-world datasets to write code to accomplish tasks such as predicting outcomes, discovering associations, and identifying similar groups. Students will complete a term project showcasing the different steps of the machine learning process, from data cleaning to the extraction of accurate models and the visualization of results. Prerequisite: CSE 274.

CSE 433/CSE 533. Deep Learning. (3)

This course introduces basic concepts for deep learning and covers the preliminary knowledge of neural networks. Students will learn to implement and train their own neural networks and gain an understanding of research in deep learning. Additionally, students will complete a final project to design and train a customized neural network on selected real- world problems. Through multiple hands-on assignments and the final course project, students will acquire the skills to perform deep learning tasks and the best practices to train and fine-tune deep neural networks. Prerequisites: CSE 274 and MTH 222 and MTH 231, or permission of instructor.

CSE 434/CSE 534. Generative Artificial Intelligence. (3)

This course introduces students to AI tools that allow the creation of new data such as text (Natural Language Generation), images, or videos. Students use Large-Scale Language Models (LLMs) to generate text, evaluate its quality, and integrate the generator in sample applications. Students also create images via deep neural networks, although no prior familiarity with deep learning is required. Students will complete a term project. Prerequisite: CSE 374.

CSE 486/CSE 586. Introduction to Artificial Intelligence. (3)

Basic concepts of artificial intelligence (AI) including problem solving, search knowledge representation, and rule-based systems covered with symbolic AI language such as PROLOG or LISP. Application areas (natural language understanding, pattern recognition, learning and expert systems) are explored. Prerequisite: CSE 274 or equivalent and (MTH 231 or MTH 331).

ENG 171. Humanities and Technology. (3)

Introduction to methods of thinking used in humanities disciplines (literature, history, philosophy, classics, etc.), computer technologies, and their relationship. Practical skills (web page making; research on the Internet) and analytical skills (how to tell good information from bad) combined with theories about the Information Society. IIB. PA-3B. CAS-B. Cross-listed with IMS 171.

ISA 630. Machine Learning Applications in Business. (3)

In this course students will learn supervised and unsupervised modeling techniques using artificial intelligence and machine learning. Methods will include ensemble modeling, customized ensembles and deep learning. The course will focus on the impact and implications of these advanced techniques in business. Prerequisite: ISA 591.