Foundations of Artificial Intelligence and Machine Learning New (AI/ML)

Module 1: Introduction to AI & ML Problems

1. Data representation and key features of ML pipelines
2. Data acquisition and preprocessing example
3. Supervised and unsupervised Learning
4. Classification and regression problems
5. Dimension reduction and clustering problems
6. Examples of machine learning approaches

Module 2: Models for Supervised Learning

1. Supervised learning review
2. Linear models
3. Nonlinear models
4. Neural network principles

Module 3: Training Models and Performance Estimation

1. Supervised learning and machine learning models review
2. Model selection and performance analysis
3. Gradient based optimization methods
4. Methods for controlling model overfitting
5. Criteria for optimization of classification models
6. Multiclass classification criteria
7. Backpropagation for training neural networks

Module 4: Clustering and Dimension Reduction

1. Unsupervised learning review
2. Clustering methods
3. Dimensionality reduction, information extraction, and noise reduction

Registration Date/Time:
4/1/2025 7:30am Central Time

Event Dates/Times:

• 4/1/2025  9:00am - 4:30pm Central Time
• 4/2/2025  9:00am - 4:30pm Central Time
• 4/3/2025  9:00am - 2:30pm Central Time

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. 

• Program Director

  Erick Oberstar

• 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. 

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.

Enroll over the phone and pay by credit card.

• Call CERC Registrations at 608-262-2451.
• Provide the registrations representative with:
  • the course name, dates, and/or course number.
  • the necessary course attendee information and payment information.
• You will receive either a mailed document or an email to confirm successful enrollment 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 interpro@union.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 interpro@union.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.

If you cannot attend, please notify us no later than one week before your course begins, and we will refund your fee. Cancellations received after this date and no-shows are subject to a $150 administrative fee. You may enroll a substitute at any time before the course starts.

At the completion of this course, participants will be able to:

1. Describe advantages and pitfalls in commonly used machine learning strategies.
2. Describe considerations in data collection and prep for machine learning
3. Explain the differences between commonly used models.
4. Describe tradeoffs between dataset size and machine learning model complexity.
5. Give examples of optimization criteria for training machine learning models.
6. Describe the best practices for using data to both train a machine learning model and predict performance.
7. Describe the best practices for optimizing complexity of a machine learning model.

This course is part of the Technical Leadership Certificate. Course may be taken individually as well.