Introduction to Machine Learning for Engineering Research

Author: Benjamin Afflerbach
Date Uploaded: 2024-10-03
Content Type(s): full course, pre-recorded lectures, slides, jupyter notebooks, and readings
Content Length: 10 weeks (1 semester)
Content Audience: undergraduate
Content Topics: data cleaning, featurization, model assessment, linear models, neural networks, deep learning, computer vision, machine learning, and undergraduate research

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Overview

This curriculum provides students an introduction to using machine learning tools and the associated necessary background on machine learning methods and statistical analysis. Throughout the curriculum, students will work thorugh hands-on example to generate and assess machine learning models. They will learn key ideas for assessing model performance and decision making skills for how to improve or modify a model.

Learning Objectives

• Students can explain how machine learning fits into common research workflows
• Students can execute common Data Cleaning, Featurization, and model Assessment tasks in Python
• Students make decisions on how to modify models to improve model quality

Additional Information

This course contains weekly modules that have pre-recorded lectures and associated slides and Jupyter notebook hands on activities. Deliverables take the form of a “weekly slide deck submission” that mirrors a research slide deck students might present to a research advisor.

Links

All Materials

ML4ER Course Webpage

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