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Course Description

In this course, engineers will learn to apply AI tools that empower a new approach to complex problems: human experts and AI models working synergistically to make predictions, detect anomalies, and optimize systems. This course includes explorations of  improving simulations using neural networks, and use of natural language processing (NLP) for requirements gathering and documentation. Case studies from the high tech industries and more will highlight benefits of AI systems for predictive maintenance and improving safety. Students will also tackle challenges arising from data quality issues and integrating these new tools with existing systems.

Learning objectives

By the end of this course, students will be able to:

  • Design simulations that incorporate neural nets and other AI models to improve accuracy
  • Evaluate NLP techniques for efficient communication and documentation
  • Develop strategies for responding to challenges in AI implementation, such as those arising from poor data quality, bias or the need to integrate new tools with existing systems

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Prerequisites

This is a credit-bearing, graduate-level, non-matriculated course.
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Enroll Now - Select a section to enroll in
Section Title
Special Topics: AI Applications in Engineering
Type
Online/Asynchronous
Dates
Jan 27, 2025 to Mar 07, 2025
Delivery Options
Course Fee(s)
Available for Credit
3 units
Drop Request Deadline
Jan 26, 2025
Transfer Request Deadline
Jan 26, 2025
Section Notes
Students who enroll in this course are attesting that they have a bachelor’s degree.
Section Title
Special Topics: AI Applications in Engineering
Type
Online/Asynchronous
Dates
Mar 26, 2025 to May 07, 2025
Delivery Options
Course Fee(s)
Available for Credit
3 units
Drop Request Deadline
Mar 25, 2025
Transfer Request Deadline
Mar 25, 2025
Section Notes
Students who enroll in this course are attesting that they have a bachelor’s degree.
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