Educational Programs
Mini-MBA
The Bauer Mini-MBA program on Analytics and AI is a week-long program that comprise both business core courses and two days of courses on Analytics and AI. This will serve as pathways for the students to apply to our MBA or EMBA programs.
Undergraduate Certification Program
Issues surrounding how society uses technology and data-driven insights have become more critical than ever, with a global focus on upskilling and preparing the workforce for the jobs of tomorrow.
Bauer College has collaborated with Intel to provide training in Artificial Intelligence that is accessible, equitable and applicable across all industries. Together, we are empowering non-technical audiences to harness the power of AI across disciplines, including accounting, energy, healthcare, real estate, finance, supply chain and beyond.
Stay connected with us as we announce upcoming undergraduate and executive development programs in AI.
Customized Programs
Customized programs in AI and Analytics are offered for specific corporate partners, for their employee training of certain important topics. Interested companies may reach out for information on the customized programs.
Courses curated by our expert faculty which cover various topics related to AI. Certificates and endorsement of skills will be awarded upon completion of the course. Each course is a 1 day program with an open enrollment option (email HCAII@uh.edu for more information). Please enroll in the course you are interested in; now offering enrollment for AI in Energy and AI in Education.
AI in Energy
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COURSE OBJECTIVE: This program serves as an introductory AI course tailored for business and engineering professionals within the energy sector who lack a background in AI. It aims to establish a foundational understanding of AI technologies, highlighting their benefits, drawbacks, and potential pitfalls. The course maintains a strong focus on the practical applications of these technologies within the energy industry. It covers both traditional energy sectors, which are anticipated to phase out by 2035, and the burgeoning renewable energy sectors. The curriculum will explore how AI technologies are currently being used and how they are expected to critically influence the transition from today's practices to the energy solutions of tomorrow. Emphasizing AI-driven engineering innovations, the course will illustrate how these advancements are poised to elevate the energy industry to new heights.
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COURSE OUTLINE:
| Lecturer | Topic | Project | Time |
|---|---|---|---|
| Meng Li | Introduction | AI & Machine Learning | 9-9:10 a.m. |
| Coffee & Refreshment | 9:10-9:30 a.m. | ||
| Wei Li | Digitalization: Streamlined Procedure | Prep I for Energy AI | 9:30-10:30 a.m. |
| Wei Li | Institutionalization: Knowledge | Prep II for Energy AI | 10:30-11:30 a.m. |
| Lunch Break | 11:30 a.m.-12 p.m. | ||
| Wei Li | ML for SCM Apps at OG&C | Upstream Logistic Risks | 12-1 p.m. |
| Wei Li | Upstream Logistic Plan Case Study | Bottlenecks/snowballs | 1-2 p.m. |
| Wei Li | Simulated Scenarios & Objectives | Operation Scenarios | 2-3 p.m. |
| Wei Li | AI for Offshore Wind Power Industry | Forecasting | 3-4 p.m. |
| Wei Li | Offshore Wind AI Case Study | Smart Planning | 4-5 p.m. |
Lecturer: Wei Li
9-9:10 a.m.
Introduction
Project: AI & Machine Learning
9:10-9:30 a.m.
Coffee & Refreshment
9:30-10:30 a.m.
Digitalization: Streamlined Procedure
Project: Prep I for Energy AI
10:30-11:30 a.m.
Institutionalization: Knowledge
Prep II for Energy AI
11:30 a.m.-12 p.m.
Lunch Break
12-1 p.m.
ML for SCM Apps at OG&C
Project: Upstream Logistic Risks
1-2 p.m.
Upstream Logistic Plan Case Study
Project: Bottlenecks/snowballs
2-3 p.m.
Simulated Scenarios & Objectives
Project: Operation Scenarios
3-4 p.m.
AI for Offshore Wind Power Industry
Project: Forecasting
4-5 p.m.
Offshore Wind AI Case Study
Project: Smart Planning
The above scheduled projects may be inserted with a 5 minute break in between.
AI in Education
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COURSE OBJECTIVE: This course is designed for teachers who want to understand Generative AI (GenAI), its future, application to work, and implications. We will build a working knowledge of the underlying technology of GenAI and discuss the form's future developments may take. We will learn to apply GenAI to teaching through supervised group projects; projects will include work in content development, student simulation, and research methods. The course will develop the tools needed to make ethical choices surrounding AI; some relevant tools are AI detection methods, safeguards to ensure accurate, unbiased output from GenAI, and insight from top professionals and academics.
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COURSE OUTLINE:
| Lecturer | Topic | Project | Time |
|---|---|---|---|
| Meng Li | Introduction | 9-9:10 a.m. | |
| Coffee & Refreshment | 9:10-9:30 a.m. | ||
| Josh Kaisen | AI-Driven Decision Making | 9:30-10:30 a.m. | |
| Josh Kaisen | Getting Started with LLMs | 10:30-11:30 a.m. | |
| Lunch Break | 11:30 a.m.-12 p.m. | ||
| Josh Kaisen | LLMs and Teaching | ChatGPT Exam | 12-1 p.m. |
| Josh Kaisen | ChatGPT Projects | Engagement | 1-2 p.m. |
| Josh Kaisen | LLM Detection Methods | LLM Detection | 2-3 p.m. |
| Josh Kaisen | Adding AI to Assignments | ChatGPT Exam | 3-4 p.m. |
| Josh Kaisen | AI Issues and the Future of AI | 4-5 p.m. |
Lecturer: Josh Kaisen
9-9:10 a.m.
Introduction
9:10-9:30 a.m.
Coffee & Refreshment
9:30-10:30 a.m.
AI-Driven Decision Making
10:30-11:30 a.m.
Getting Started with LLMs
11:30 a.m.-12 p.m.
Lunch Break
12-1 p.m.
LLMs and Teaching
Project: ChatGPT Exam
1-2 p.m.
ChatGPT Projects
Project: Engagement
2-3 p.m.
LLM Detection Methods
Project: LLM Detection
3-4 p.m.
Adding AI to Assignments
Project: ChatGPT Exam
4-5 p.m.
AI Issues and the Future of AI
PROJECT DESCRIPTIONS:
ChatGPT Exam: Prompt ChatGPT to create a short answer exam. Use ChatGPT to polish the exam. Then have ChatGPT answer created questions as a student of various levels. Finally, use ChatGPT answers to construct an objective grading standard.
Engagement: Use ChatGPT to create content suited to your or your students' interests
AI Detection: Practice using AI detection methods to figure out which samples are AI generated