This reading covers the professional and ethical obligations engineers have when using AI tools such as large language models and code-generation assistants, guiding learners in critically evaluating, validating, and responsibly integrating AI outputs into engineering work.
Topics
This module covers 5 topics. Open a topic to see its subtopics.
Module Content
6 items.
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This reading introduces the core concepts of supervised machine learning—including labeled training data, model fitting, prediction, and generalization—with engineering-relevant examples, serving as a conceptual foundation for learners before applying ML techniques.
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This reading explains how to use the scikit-learn Python library to execute the full machine learning workflow—data preprocessing, model training, evaluation, hyperparameter tuning, and result interpretation—providing learners and instructors with a practical implementation reference.
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This reading explains how to design and implement engineering automation scripts and parameter sweep pipelines in Python to replace repetitive simulation and data-processing tasks, teaching learners to systematically explore design spaces and automate report generation.
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This reading covers the fundamentals of Git version control and GitHub collaboration workflows—including commits, branches, and repositories—so learners can manage code changes and collaborate on engineering scripts and automation projects professionally.
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This reading covers the professional and ethical obligations engineers have when using AI tools such as large language models and code-generation assistants, guiding learners in critically evaluating, validating, and responsibly integrating AI outputs into engineering work.