Foundations of Artificial Intelligence and Machine Learning New (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.

• 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

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

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

Barry Van Veen

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.