Course description

Covers the basic concepts, models, methods, and applications of deep learning. Topics include basics of artificial neural networks, training of neural networks, convolutional neural networks, recurrent neural networks, generative models, deep reinforcement learning, and deep learning hardware and software packages. Application and methodology topics include deep learning for pharmaceutical discovery, deep learning for process control, deep learning for molecular design, deep learning for material screening, deep learning for product yield and quality estimation, and deep learning for optimization.

Students signing up for 3 credits will be working on one course project, while those with 4 credits should work on two course projects. There is no exam in this course, and students will be evaluated by the project outcome. Cloud computing credits will be provided to students to leverage deep learning hardware.


A preliminary machine learning course, such as ChemE 6880 or SysEn 5880

No upcoming classes were found.

Previously offered classes

The next offering of this course is undetermined at this time.