AI for Engineers

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

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.

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