CHEME 6888 - Deep Learning

(crosslisted) SYSEN 6888

Fall, Spring. 4 credits. Letter grades only.

F. You.

This course provides a comprehensive overview of deep learning, covering basic concepts, models, algorithms, and applications. Topics include artificial neural networks, training techniques, convolutional neural networks, recurrent neural networks, generative deep learning, deep reinforcement learning, and deep learning hardware and software. Recent advances in deep learning, such as graph neural networks, attention, Transformer, ViT, BERT, and GPT, will also be discussed. The course explores deep learning-based applications in optimization, sensing, control, and automation, and in AI for Science, including molecular design, material discovery, and pharmaceutical development.

Outcome 1: Analyze and understand modern deep learning models, algorithms, and applications.

Outcome 2: Demonstrate ability to develop deep learning models and algorithms for real-world applications.

Outcome 3: Demonstrate ability to apply deep learning to solve application problems.