EM 589 Artificial Intelligence for Engineering Managers

3 Credit Hours

The course begins with a history of Artificial Intelligence explaining why things evolved as they did. A survey of AI methods and algorithms will then be covered, including machine learning, deep learning, generative AI and transformers, computer vision, natural language processing and large language models (LLMs). The objective is to train the engineering manager to understand the scope and limitations of each technique. An overview of data science, including the identification of relevant data sets will be included. Applications of AI will be addressed including but not limited to AI in FinTech, AI in Healthcare and AI to understand buyer behavior. By the end of the course, students will be able to estimate compute resources required for a project, create a project plan, and estimate the costs and time required, including the types of people and amount of labor needed, and students will understand AI and relevant terminology sufficient to communicate and lead a team of people, and to assess the impact of a given application on the firm, the workplace, or the economy.
This is not a course that teaches the student to be a machine learning or AI engineer. Students may or may not have that as their prior background. This course focuses on how to deploy and manage AI projects and how to make decisions about whether to adopt AI for a particular problem or product. Students will learn to create project plans for AI products and projects. In addition, students will learn to assess the impact of the disruptive changes of AI on the workplace and the economy.

Prerequisite(s)

Admission to the MEM Program or an engineering management graduate certificate. Other engineering graduate students, including Engineering Online students may take the course with permission from their advisor or program director. Some knowledge of statistics, machine learning, and/or Python are helpful, but not required.

Course Topics

● History of AI
● Limitation of AI
● Terminology for AI
● Introduction to machine learning and deep learning
● Overview of computer vision
● Overview of natural language processing
● Introduction to Generative AI and Transformer Architectures
● Generative Adversarial Networks
● Exposure to agent-based models
● Survey of other AI methods: Bayesian Reasoning, Genetic Algorithms, Expert Systems
● Relationship with the field of signal processing, pattern recognition and data analytics
● Open-Source Tools and Licensing
● Sourcing and Managing Data, Managing Rights
● Scrubbing and cleansing of data, and cost estimation
● Re-use of AI Models
● LLMs, Prompt Engineering and Responsible Use, ChatGPT
● Adopting LLMs in an Organization
● Computer Power Required for Training LLMs and AI Models
● Disruptive Nature of AI and Impact on the Work Force
● Impact of AI on the Economy
● Great Debates about AI and Society, Ethics, Perils
● Product Management for AI Projects
● Choosing Tools, data sets, people
● Project Management Planning for AI projects and products
● Building an AI Native Product
● Applications: Market Analytics, FinTech, Drug Discovery, Sports Analytics, Law
● Application to Scientific research
● Future of AI

Course Objectives

By the end of the course, students will be able to
● Identify areas where AI will provide a technical or competitive advantage
● Pose the question or problem that an AI system is intended to solve
● Determine data requirements for an AI or machine learning system including type, quality, quantity
● Source data sets internally, on the web or externally through vendors
● Explain the major AI paradigms including neural networks, deep learning, transformers, generative AI, large language models, natural language processing and computer vision
● Understand which paradigm is applicable and why
● Understand responsible uses of LLMs and how to train and implement them
● Determine whether a novel algorithm is needed or if an open-source algorithm may be used, and assist in choosing which one Deleted: AI
● Manage the training of a machine learning system, followed by performance measurement, tuning and improvement
● Estimate compute resources and cost necessary for a particular AI project
● Create a project plan for an AI project including staffing, labor loading, Gantt chart, development, training, performance evaluation, tuning and deployment

Course Requirements

Expectations:
Your success in the class depends on a mix of learning from others and developing ideas and concepts of your own. The course requires learning from assigned readings, threaded discussions, and a term project to develop a product launch plan. Students are expected to complete reading assignments (available on the course website) before viewing the twice-weekly posted videos. You will need to provide a webcam and headset (earphones and microphone) to record your final presentation at the end of the course. You must complete the requirements under Grading below.
Grading:
Grades are a necessary part of earning a degree. That said, this is an elective course, and we hope you are here to learn and have fun. Your grade is based on the following requirements and weightings.

Participation: 25%
Online Quizzes: 20%
Assignment #1: 15%
Assignment #2: 15%
Assignment #3: 25%

Textbook

Required. The following books are required for the course, total cost of all 4 is ~$80:

Artificial Intelligence for Managers, “Leverage the Power of AI to Transform Your Organization”, Malay Upadhyay, bpb, 2021, ISB: 9789389898385

Introduction to LLMs for Business Leaders, I. Ameida, Now, 2023, ISBN: 9780645510584

Technomics: Life Changing Economics of Disruptive Technologies, Toasha Wang, 2023

The AI Product Manager’s Handbook, Irene Bratsis, packt, 2023, ISBN: 978-1-80461-293-4

Created 5/2/2024