New for 2022!


Today, widespread Machine Learning (ML) in the Utility industry has become more prevalent than imagined a few short years ago. This training introduces ML, including the concepts of exploratory data analysis, supervised learning (classification and regression), and unsupervised learning (dimensionality reduction and clustering). Participants will apply concepts through guided, hands-on activities.

Earn a Certificate of Participation and Completion and Continuing Education Units (CEUs) from the University of Oklahoma.

Upon Completion of this Training, Participants will be able to:

  • Provide an overview of machine learning and related tools and topics.
  • Apply exploratory analysis, supervised learning, and unsupervised learning techniques towards industry use cases.
  • Be able to use predictive analytics methods to produce insights or solutions to a problem, given appropriate datasets and tools.
  • Understand how to evaluate and improve models and perform error detection/correction.

Who Should Attend?

  • Analytics professionals who are interested in machine learning methods with applications in utilities.

  • Utility Analytics 101 completers who want to continue advancing their in-depth knowledge of analytics in the utilities setting.

Why Attend?

UAI built this course with guidance from data science and analytics experts that are members of our Strategic Advisory Board and Executive Advisory Council. We've partnered with The University of Oklahoma Data Science and Analytics Institute (OU DSAI) to develop and deliver a training that delivers solid ROI and will help attendees stay ahead of the game.

Attendees will earn a Certificate of Participation and Completion and Continuing Education Units (CEUs) upon completion of the course.

Applied Machine Learning for Utility Professionals

DAY 1:

Brief Machine Learning Overview; 
Preparing for Machine Learning;
Hands-on Application

DAY 2:

Supervised Learning;
Assessing & Improving Performance;
Learning Algorithm Selection;
Hands-On Application

DAY 3:

Advanced Methods;
Challenges in Machine Learning;
Ethical Concerns and Identifying Biases;
Hands-on Application

DAY 4:


Unsupervised methods;
Time Series Modeling;
Hands-on Application