AI Engineering

Units: 12 units

Learn fundamental artificial intelligence and machine learning techniques for developing software that is foundational to next-generation design and analysis tools. In this course, you will explore topics like supervised and unsupervised learning, feature engineering, model selection and optimization, dimensionality reduction, and ensemble learning, and then complete the course with an introduction to deep learning. You’ll not only learn the theory behind these techniques, but how to efficiently implement them as well.

Units: 12 units

Through hands-on activities, you will learn the foundations of deep neural networks, their applications to engineering tasks, and how to use deep learning to solve complex engineering problems. In this course, you will explore topics like convolutional neural networks, recurrent neural networks, long short-term memory, and generative adversarial networks.

Units: 12 units

Explore advanced AI models used in engineering applications. Topics in this course build upon the core deep learning models taught in the AI Engineering Fundamentals certificate and include: advanced variants of convolutional neural networks, graph neural networks, generative adversarial networks, neural operators, physics-informed neural networks, and diffusion models. By the end of this course, you should know how to use these models in a wide range of engineering applications including surrogate modeling, materials discovery, engineering design, manufacturing, and human-AI teaming. 

Coursework emphasizes the theoretical foundations and the mathematical modeling of the introduced techniques along with the implementation and testing of these techniques in software. Assignments require knowledge of Python and PyTorch at the level used in 24-888 Introduction to Deep Learning.

Units: 12 units

This course focuses on generative AI models used in engineering applications and focuses on topics like diffusion models, foundation models, transformers and large language models. The course connects these algorithms to publicly available generative AI systems and teaches you how these systems can be tailored toward engineering applications. Each topic culminates in a mini-project where you will build upon existing software to design and implement these techniques focusing on an engineering application. 

The coursework emphasizes the theoretical foundations and the mathematical modeling of the introduced techniques. Assignments include quizzes that assess a conceptual understanding of these topics as well as several guided projects that focus on software implementation, validation, and technical reporting. The assignments require knowledge of Python and PyTorch at the level used in 24-880 Advanced AI Models in Engineering.