AI for Engineering

  • Overview
  • Course Content
  • Requirements & Materials
Overview

AI for Engineering

Course Description

Fariborz Farahmand, research faculty in Georgia Tech’s School of Electrical and Computer Engineering, teaches this artificial intelligence course that explores applications for engineers through hands-on projects and lectures. By employing simple terms and practical examples, learners gain a strong understanding of AI’s strengths and limitations in engineering contexts. During the live, online AI training, learners will build a conceptual and computational understanding of AI engineering systems and their real-world applications. Case studies enhance discussions by illustrating how artificial intelligence addresses complex engineering problems. Class projects help learners gain AI skills that they can apply to industry-specific challenges. Participants engage with the instructor for feedback and guidance during the case studies and project work, preparing them to lead AI engineering teams and initiatives.

Course Content

Module 1: Introduction to AI 

Covering overview of AI tailored for engineering applications, AI agents, supervised, unsupervised, and reinforcement learning, and AI standards

Module 2: Bayesian Networks 

Building a rigorous and efficient formalism for representing uncertain knowledge for probabilistic reasoning in engineering systems 

  • Sample case study: Bayesian networks for digital twins

Module 3: Deep Learning 

Understanding core concepts of deep learning and artificial neural networks, how deep learning works, and popular deep learning architectures 

  • Sample case study: Convolutional Neural Networks (CNNs) for engineering design

Module 4: Physics Informed Neural Networks (PINNs) 

Integrating engineering knowledge of the physical world into state-of-the-art data analysis, and constructing computationally efficient physics-informed surrogate models with limited data 

  • Sample case study: PINNs for structural mechanics

Module 5: Generative AI 

Understanding Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) and their core concepts, architectures, training processes, and practical applications 

  • Sample case study: GANs for manufacturing

Module 6: AI Safety and Security and Causal Inference 

Understanding the entanglement of cybersecurity and safety in the AI era, and how engineers and computer scientists can collaborate, using Turing-Award (Nobel Prize of Computing) winning causal analysis tools 

  • Sample case study: A systems engineering approach to autonomous vehicles safety

Module 7: Human-Centered AI 

Understanding the fundamentals of human-centered AI and exploring human-centric approaches to AI systems in Industry 5.0 

  • Sample case study: Thinking fast and slow for AI systems design
Requirements & Materials

Prerequisites

RECOMMENDED:

Previous coursework in probability and statistics

Materials

PROVIDED (student will receive):

Case studies and lecture materials will be provided.

Session Details

Who Should Attend

This course is designed for engineering, computer science, and risk management professionals and executives who want to build or strengthen AI skills within engineering contexts and advance their careers in today’s AI-driven job market.

Computer science students coding on computers

What You Will Learn

  • Core Artificial Intelligence (AI) concepts for engineering
  • Foundations of AI systems and their components
  • Assessment of AI using engineering, computer science, and risk management methods
  • Identification of opportunities to apply AI in engineering processes
  • Real-world AI case studies analysis
  • Application of AI techniques to industry-specific projects
  • Integration of engineering knowledge in data-driven approaches
Analytics professional learning on computer and laptop

How You Will Benefit

  • Explore practical applications of Artificial Intelligence (AI) in solving complex engineering problems.
  • Apply domain-specific engineering expertise when collaborating on interdisciplinary AI challenges.
  • Strengthen your ability to work across engineering and AI teams.
  • Develop skills for designing more innovative, robust, and data-driven engineering solutions.
  • Advance your career in today’s AI-powered engineering landscape.
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  • Taught by Experts in the Field icon
    Taught by Experts in the Field

FREQUENTLY ASKED QUESTIONS

TRAIN AT YOUR LOCATION

We enable employers to provide specialized, on-location training on their own timetables. Our world-renowned experts can create unique content that meets your employees' specific needs. We also have the ability to deliver courses via web conferencing or on-demand online videos. For 15 or more students, it is more cost-effective for us to come to you.

  • Save Money
  • Flexible Schedule
  • Group Training
  • Customize Content
  • On-Site Training
  • Earn a Certificate
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