AI for Engineers
Course Overview
The eight-week AI for Engineers online course from Michigan Engineering Professional Education equips participants to design, build, and deploy scalable, production-ready AI systems.
As organizations shift from AI experimentation to real-world deployment, many teams struggle with integration, reliability, and scalability. This program bridges the gap by combining core AI concepts with modern engineering practices, including machine learning pipelines, MLOps, and responsible AI.
By the end of the program, participants gain the skills and confidence to engineer AI solutions that deliver measurable business impact.
Why Enroll in the AI for Engineers Course?
Join two live sessions with Michigan instructors and access an e-learning platform featuring self-paced activities, readings, and recorded videos.
Expand your knowledge and gain cutting-edge insights and practical tools to tackle complex technology and innovation challenges.
Gain valuable insights into AI’s practical applications and strategies in engineering through real-world examples and in-depth case studies—staying at the forefront of innovation in design optimization and autonomous systems.
Build meaningful professional connections with peers and experts, fostering collaboration and networking opportunities.
The program includes two live online sessions with faculty, interactive discussion forums, and access to an innovative e-learning platform.
Key Takeaways of AI for Engineers Course
Strong understanding of machine learning methods applied to real-world engineering problems.
Hands-on experience building and validating models, from setup and training to data visualization.
Apply AI-driven optimization and simulation to improve product design and development.
Use AI for autonomous control and intelligent operation of mechanical systems.
Identify high-impact AI use cases and implement the right AI frameworks.
Who Is This Course For?
Engineers and engineering professionals looking to apply AI and machine learning to real-world engineering challenges, enhancing efficiency, performance, and innovation across design, manufacturing, energy, autonomous systems, and related fields.
Managers and executives in engineering-intensive organizations who want a clear, strategic understanding of how AI can drive innovation, operational transformation, and long-term business value.
Technical professionals with hands-on engineering experience who want to build practical AI capabilities and apply advanced machine learning techniques in engineering contexts, without requiring formal training in data science or software development.
Program Modules
Focus: Understanding where ML adds value in engineering systems.
What you learn
How ML reduces computational cost in physics-based simulations
How data-driven models accelerate design iteration
How ML enables exploration of high-dimensional design spaces
Pattern discovery in materials, components, and system performance data
What you do
Analyze real engineering use cases (wind turbines, battery electrodes, materials design)
Compare traditional simulation approaches vs. ML-augmented workflows
Articulate measurable engineering benefits of ML adoption
Skills you’ll build
Translating engineering problems into ML-amenable formulations
Technical communication of AI value to engineering stakeholders
Focus: Turning an engineering process into an ML system.
What you learn
Supervised vs. unsupervised vs. semi-supervised vs. reinforcement learning
Data types common in engineering (sensor, time-series, images, logs)
Model lifecycle: training, validation, deployment, monitoring
What you do
Take a real engineering workflow (fault detection, inspection, monitoring)
Design a full ML solution:
Data pipeline
Model choice
Evaluation metrics
Deployment strategy
Skills you’ll build
ML system design thinking
Data readiness assessment
Model performance evaluation (metrics, validation strategies)
Focus: Modern AI models engineers actually use.
What you learn
Deep learning architectures (ANNs, CNNs, RNNs) for engineering, image, and time-series data
Hyperparameter tuning, optimization strategies, and generalization tradeoffs
GAN fundamentals, including generator–discriminator dynamics and training challenges
What you do
Map deep learning models to different engineering data types
Tune and optimize models for performance, stability, and cost
Build and train a GAN from scratch in PyTorch using MNIST/CIFAR-10
Visualize model outputs, loss curves, and training behavior
Skills you’ll build
PyTorch model development
GPU-accelerated training
Debugging and interpreting deep learning models
Focus: AI for engineering design space exploration.
What you learn
AI-based design optimization concepts
Generative design principles
Prompt evolution and iterative optimization
What you do
Implement a simplified Prompt Evolution Design Optimization (PEDO) framework
Generate and optimize car designs in a Jupyter Notebook
Critically evaluate model outputs and propose improvements
Skills you’ll build
AI-assisted design workflows
Evaluating generative model quality
Engineering judgment applied to AI outputs
Focus: AI in control, robotics, and autonomy.
What you learn
AI for autonomous decision-making
Challenges in real-time systems
Tradeoffs in autonomy vs. safety and reliability
What you do
Analyze AI applications in autonomous vehicles and robotics
Discuss system-level challenges and opportunities
Skills you’ll build
Systems thinking for AI-enabled autonomy
Risk and limitation assessment
Focus: Industrial AI for Industry 4.0.
What you learn
Autoencoders and denoising autoencoders
Reconstruction error–based anomaly detection
Model robustness and generalization
What you do
Implement autoencoders in PyTorch
Detect anomalies in synthetic manufacturing sensor data
Progress from basic implementation to advanced optimization:
Architecture tuning
Regularization (dropout)
Learning rate schedules
Performance metrics
Skills you’ll build
Industrial anomaly detection
Model tuning and experimentation
Practical ML troubleshooting
Focus: Large-scale, real-world engineering impact.
What you learn
Predictive maintenance in energy systems
Sensor-driven ML pipelines
Cost-benefit and ROI analysis of AI systems
What you do
Design an AI predictive maintenance system for a wind farm
Select sensors, ML models, and deployment strategy
Estimate cost savings, uptime improvements, and ROI
Skills you’ll build
End-to-end AI system design
Engineering + business decision-making
Translating AI performance into financial outcomes
Focus: Applying engineering AI skills in regulated, high-stakes domains.
What you learn
CNNs in medical imaging
Integration challenges across engineering and healthcare
Ethical and technical constraints
What you do
Analyze real medical AI applications
Assess integration challenges from an engineering perspective
Skills you’ll build
Cross-domain AI application
Ethical and technical risk assessment
Learning Objectives of the AI for Engineers Course
Grasp AI fundamentals and its applications in mechanical engineering.
Explore AI mechanical engineering applications like design optimization, manufacturing, and energy.
Master machine learning for modeling, optimization, and analysis.
Work with large datasets using advanced visualization tools.
Analyze real-world AI mechanical engineering applications through case studies across industries.
Digital Certificate
Upon successful completion of this course, participants will receive a certificate from the Michigan Engineering Professional Education. A digital badge is also awarded, which can be shared on LinkedIn or added to a professional portfolio.
Note: Certificates and digital badges are issued in the name used during course registration. Images are for illustrative purposes and may be updated at the discretion of the University of Michigan.
Learning Experience
Experience flexible learning with self-paced activities, recorded videos, and readings. Join live webinars and interactive sessions with peers and instructors for real-time insights. Dive deeper through engaging forums for collaborative hands-on activities. University of Michigan offers flexibility, interaction, and real-world learning. In summary, your learning experience includes:
Live webinars & interaction with instructors and peers
Self-paced activities, readings, and recorded videos
Engaging discussion forums
Innovative e-learning platform
Instructor
Professor, Mechanical Engineering | ME Associate Chair for Facilities and Planning
Wei Lu is a Professor at the Department of Mechanical Engineering, University of Michigan - Ann Arbor. He received his Ph.D. from Princeton University and his B.S. from Tsingh...
The Michigan Difference
#1
#3
#18
FAQs
AI for Engineers is designed for engineering professionals, graduates, and students across manufacturing, energy, automotive, aerospace, biomedical, and related fields, as well as technical managers and leaders seeking applied AI knowledge.
No. The course begins with AI fundamentals and builds toward practical applications. In AI for Engineers, prior experience in AI or data science is not required.
Yes — AI for Engineers includes live webinars and interactive sessions with faculty and peers, giving you real-time insights, opportunities to ask questions, and direct engagement with instructors
You’ll learn how to apply AI and machine learning techniques to real engineering problems, including design optimization, predictive maintenance, autonomous systems, and data-driven decision-making.
Unlike generic AI courses, AI for Engineers course is engineering-focused and application-driven, emphasizing hands-on projects, real-world case studies, and open-source tools.
The course explores applications across manufacturing, energy systems, robotics, autonomous vehicles, generative design, and biomedical engineering.
The course balances both. In AI for Engineers course, participants gain hands-on technical exposure while also learning how to identify high-impact AI opportunities and communicate value within organizations.
The course is delivered online over eight weeks, combining video lectures, assignments, discussion forums, and applied case studies.
Participants are evaluated through practical assignments (70%) and forum participation (30%), with a strong focus on applied learning.
Upon successful completion, learners earn a Certificate of Completion from Michigan Engineering Professional Education.
AI for Engineers helps you future-proof your engineering skill set, enabling you to contribute to AI-driven initiatives and drive innovation in engineering-led organizations.
Applicable taxes will be calculated and added at checkout in accordance with country/state regulations.
Connect with a Program Advisor for a 1:1 Session
Didn't find what you were looking for? Schedule a call with one of our Program Advisors or call us at +13159021796.
Register now and boost your professional trajectory.
Enroll by