Prerequisites: 6.036, 6.041 or 6.042; 18.06
Instructors: Prof. Phillip Isola (firstname.lastname@example.org)
Schedule: TR1-2:30, room 4-231
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.