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

Artificial Intelligence (AI) and Machine Learning (ML) enable businesses to automate repetitive tasks, improve operational efficiency, predict market trends, identify risks, and use data to make decisions. This course teaches managers and technical professionals how AI and Machine Learning tools and strategies work, so they can make decisions about how to use them. Gain an understanding of machine learning fundamentals, including supervised and unsupervised learning, classification and regression, dimensionality reduction, and clustering algorithms. Learn about data acquisition, data preprocessing and labeling, dataset sizing, machine learning models, and optimization criteria.

Upcoming dates coming soon!

Take course number RA01851 when it’s offered next.

What You Will Learn:

Learn how AI and machine learning strategies are applied and assessed; understand their strengths and limitations. For managers, engineers, technical professionals.

Who Should Attend:

  • Engineers, scientists, and technical professionals of all levels who want to learn AI and machine learning fundamentals
  • Managers, management trainees, management track professionals, and team leaders who need to understand AI and ML to make business decisions
  • Familiarity with calculus, linear algebra, and statistics is recommended

Keep Up With This Course

Course Details: RA01851

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Course Outline

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

Program Director & 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. 

  • Program Director

    Erick Oberstar

Total Credits:
CEU 1.6
PDH 16
Certificate Compatibility:

Course Administration

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Payment Options

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

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.

Course Notes

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