6.S898 Deep Learning

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Graduate Level
Units: 3-0-3
Prerequisites: 6.036, 6.041 or 6.042; 18.06
Instructors: Prof. Phillip Isola (phillipi@mit.edu)
Schedule:  TR1-2:30, room 4-231
 
Description
 
Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, transformers), backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics. Each lecture will be from a different invited expert in the field.