Interdisciplinary Professional Programs
College of Engineering

Machine Learning for Engineers The Methods Behind Modern AI

Maching learning is the part of artificial intelligence that learns from data. It's how modern AI systems, including large language models, are trained. This course is for engineers and technical professionals who want to build and evaluate those models, not just discuss them. Working with engineering datasets, participants prepare data, fit regression and classification models, train them, and test whether the result holds up on data the model has not seen. The course covers linear and nonlinear models, neural network principles, gradient-based training, clustering, and dimension reduction. You'll learn to tell a model that generalizes from one that only fits the data it was trained on.

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Who Should Attend?

  • Engineers, scientists, and technical professionals who will build, train, or evaluate machine learning models on their own data.
  • Technical leads who review models their teams build and need to judge whether a reported performance figure holds up.
  • Not the right fit if you're looking for generative AI or prompting skills, or a non-technical overview of AI strategy. See Artificial Intelligence: Strategy and Governance.

What You Will Learn

  • Frame an engineering question as regression, classification, clustering, or dimension reduction, and choose an approach that fits.
  • Prepare data for modeling and explain how scaling, feature choices, and class imbalance affect the result.
  • Fit linear, regularized, and tree-based models for regression and classification, and explain how neural networks are trained with gradient-based methods.
  • Select among models using cross-validation and performance on held-out data, and detect and control overfitting.
  • Reduce dimensionality with PCA and SVD, and group data with clustering methods.
  • Weigh data collection cost, model complexity, and optimization effort for real application.

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