Foundations of Artificial Intelligence and Machine Learning (AI & ML)

This foundational course introduces the principles and practices of artificial intelligence and machine learning, with a focus on real-world applications in engineering, operations, and business strategy. Participants will explore supervised and unsupervised learning, model optimization, and data preparation through hands-on examples and case studies. Designed for both technical professionals and managers, the course bridges the gap between theory and implementation, enabling smarter, data-driven decision-making.

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Who Should Attend?

Engineers, scientists, and technical professionals seeking to build foundational AI/ML knowledge.
Managers and team leaders who need to evaluate and implement AI/ML strategies in their organizations.
Professionals with a basic understanding of calculus, linear algebra, and statistics looking to apply data science in practice.

What You Will Learn

Understand and compare key AI and machine learning approaches, including their strengths, limitations, and use cases.
Train and evaluate machine learning models using supervised learning techniques and performance metrics.
Identify trade-offs in data collection, model complexity, and optimization strategies for real-world applications.
Recognize attributes of chat-based large language models and use them to develop machine learning models for data.

Course Details: RA01851-E137

Module 1: Introduction to AI & ML Problems

Data representation and key features of ML pipelines
Data acquisition and preprocessing example
Supervised and unsupervised Learning
Classification and regression problems
Dimension reduction and clustering problems
Examples of machine learning approaches

Module 2: Models for Supervised Learning

Supervised learning review
Linear models
Nonlinear models
Neural network principles

Module 3: Training Models and Performance Estimation

Supervised learning and machine learning models review
Model selection and performance analysis
Gradient based optimization methods
Methods for controlling model overfitting
Criteria for optimization of classification models
Multiclass classification criteria
Backpropagation for training neural networks

Module 4: Clustering and Dimension Reduction

Unsupervised learning review
Clustering methods
Dimensionality reduction, information extraction, and noise reduction

Course Location

Pyle Center

702 Langdon Street
Madison, WI 53706

Map

608-262-1122

866-301-1753

Lodging

DoubleTree by Hilton Madison Downtown

525 West Johnson Street
Madison, WI 53703

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Accommodations include: Enjoy complimentary business center, WiFi and Local/Airport Shuttle. Each room has a refrigerator, microwave, 42-inch HDTV, and executive desk. Parking available $15/night.

Online Reservations

800-222-8733

608-251-5511

Madison Concourse Hotel and Governor's Club

One West Dayton Street
Madison, WI 53703

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Room: rates start at $160

Accommodations include: Complimentary WiFi, Local/Airport Shuttle, Pool, and Fitness Center.

Fluno Center

601 University Avenue
Madison, WI 53706

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Accommodations include: Your stay includes hot breakfast buffet, fitness center, business center, convenient on-site parking. Guest rooms are complete with Keurig coffee maker, refrigerator, complimentary high-speed Internet, and 40-inch, flat screen, high-def TVs.

Online Reservations

877-773-5866

608-441-7117

More Madison Lodging Options

This is a HyFlex (in-person and online) taught course. Your registration is for one teaching platform only: in-person or online. Please be prepared to attend all days either in-person or online. Contact us if you have any questions or if you need to make a change.

Registration confirmation will guide students through accessing the Canvas course site.

Students will create and log in to the Canvas course site with a NetID. Course assets such as instructional materials, participation certificates, and course evaluations will be available to all students through the Canvas course site.

The course materials are all digital and only available on the Canvas course website.

Online attendees will access sessions via the Zoom web conferencing platform. The Zoom link will be provided a few days before the course.

Please watch the email address that you provide during registration for release dates and pre-course information.

Instructors
Barry Van Veen
Lynn H. Matthias Professor Emeritus
Barry D. Van Veen (S’81-M’86-SM’97-F’02) was born in Green Bay, WI. He received the B.S. degree from Michigan Technological University in 1983 and the Ph.D. degree from the University of Colorado in 1986, both in electrical engineering. He was an ONR Fellow while working on the Ph.D. degree.
He has been with the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison since 1987 and is currently Lynn H. Matthias Emeritus Professor of Electrical and Computer Engineering. His research interests include signal processing for sensor arrays, biomedical applications of signal processing and machine learning, and instructional methods for improving STEM education.
Dr. Van Veen was a recipient of a 1989 Presidential Young Investigator Award from the National Science Foundation and a 1990 IEEE Signal Processing Society Paper Award. He served as an associate editor for the IEEE Transactions on Signal Processing and on the IEEE Signal Processing Society’s Statistical Signal and Array Processing Technical Committee and the Sensor Array and Multichannel Technical Committee. He received the Byron Bird Award for Excellence in a Research Publication from the College of Engineering in 2020. Dr. Van Veen is a Fellow of the IEEE.
In recognition of outstanding teaching, Dr. Van Veen received the 1997 Holdridge Teaching Excellence Award from the ECE Department, the 2014 Spangler Award for Technology Enhanced Instruction from the College of Engineering, the 2015 Chancellor’s Distinguished Teaching Award, and the 2017 Benjamin Smith Reynolds Award for Teaching Engineers at the University of Wisconsin. He coauthored “Signals and Systems,” (1st Ed. 1999, 2nd Ed., 2003 Wiley) with Simon Haykin.
Kyongmin Song
Senior Data Scientist
Kyong operates at the intersection of engineering and data science, turning complex AI concepts into actionable solutions. At Barr Engineering Co., he serves as Senior Data Scientist and co-leads the Digital Solutions & AI practice group, where he architects custom AI-integrated workflows for a diverse portfolio of clients. Beyond client work, he shares insights as a frequent speaker on AI and digital transformation.
Erick Oberstar
Program Director
Dr. Oberstar is a Program Director with InterPro and has over 28 years of engineering and entrepreneurial experiences. At InterPro he manages programs for and teaches in the areas of AI/ML, Electrification, and Mechatronics. He has extensive experience in embedded real time control systems, signal and image processing, robotics, automation, medical devices. He managed the UW-Madison Mechatronics Laboratory for 22 years where he taught courses in mechatronics, manufacturing automation, automatic controls, and discrete time controls.
He has previous technical roles as a Scientist in the Department of Medical Physics at UW, consultant for St. Jude Medical, and electrical engineer for Orbital Technologies Corporation and Automation Components. His numerous entrepreneurial experiences include working on blood flow quantification, night vision, bacterial detection, robotics, automation, and general product development.
Dr. Oberstar has a Ph.D in Biomedical Engineering and MS in Electrical and Computer Engineering (WEMPEC) from UW-Madison, and a BS Electrical Engineering from UW-Platteville. He has over 30 publications with over 400 citations and three patents. Dr. Oberstar is a Wisconsin Professional Engineer, Harvey Spangler Award for Technology Enhanced Education winner and judge, as well as a SPIE member and senior member of IEEE.
Program Director
Steve Nackers

Total Credits:

CEU 2,

PDH 20,

Course Administration

If you are planning to attend an Interdisciplinary Professional Programs course, payment is required at the time of registration. Below are the payment options:

Pay by Credit Card

Enroll online and pay by credit card.

Search for the course on the website and then click on the Enroll Now button from the course webpage.
Enter all necessary course attendee information and payment information on the course enrollment page.
You will receive an email to confirm successful enrollment and payment.

Pay by Check

Mail in a completed registration form and check payable to UW Madison.

Fill out a registration form (found either in the back of the course brochure you received in the mail or here).
Prepare a check, made payable to UW Madison.
Mail the registration form and check to: CERC Registrations 21 N Park St, Ste 7101 Madison, WI 53715
You will receive either a mailed document or an email to confirm successful enrollment and payment.

Pay by Purchase Order

Request Purchase Order information from registration@cerc.wisc.edu
- If a fee quote is needed, contact custserv@interpro.wisc.edu first
You will receive an invoice from UW–Madison Business Services for payment, usually within 3–5 business days

Military

If using SF-182 form, please call our registration number at 608-262-2451 or email registration@cerc.wisc.edu for details and instructions.

Event Cancellation

We reserve the right to cancel a course due to insufficient enrollment or unforeseen events. If we cancel a course, participants will be notified via email or phone and will be given the option for a full refund or to transfer their registration and any fees paid to another course. We are not responsible for non-refundable plane tickets, hotel reservations, and other travel related expenses. For enrollee Course Cancellation, refer to notes on course page.

"AI is a new technology that is driving change in many organizations. As this technology and capability evolve, it is so important for a Manager- Environmental Compliance to understand how to utilize this evolving tool as a resource and an asset to make an organization safer, more efficient, and overall, more compliant with regulations. I gained so much insight from this class as to not only understanding the advantages of this technology but also the limitations of machine learning strategies. This class truly is an eye-opening experience that will help both technical and non-technical managers navigate AI and ML." – Suzanne Wingo, Manager Environmental Compliance, US Foods
"Super informative and provides an excellent foundation for probably one of the larger technological advances of the century." – Mike Lashua, Manager - Substation Engineering, Madison Gas and Electric

Foundations of Artificial Intelligence and Machine Learning (AI & ML)

Who Should Attend?

What You Will Learn

Course Details: RA01851-E137

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Course Administration

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