Systems and Tool Chains for AI Engineering (12 units)

Principles and key trade-offs in data collection and storage, data engineering, neural network engineering, framework architectures, and managing constraints

Introduction to Machine Learning for Engineers (12 units)

Probability and Bayesian learning, generative and discriminative classification methods, supervised and unsupervised learning, neural networks, support vector machines, clustering, dimensionality reduction, regression, optimization, evolutionary computation, and search

Introduction to Deep Learning for Engineers (6 units)

Trustworthy and Ethical AI Engineering (12 units)

Understanding of different kinds of threats and concerns for deploying AI solutions in the real world, exposure to end-to-end deployment challenges, societal issues, and policy challenges in realizing these; as well as exposure to best practices for avoiding these concerns.

Project Course (12 units)

12-770 Autonomous Sustainable Buildings: From Theory to Practice