Lecture Notes

Lecture 2: Supervised Learning

48 minute read

Ingredients of supervised learning, linear regression, binary classification, logistic regression, why learning works

56 minute read

MLP, Backpropagation, Gradient Descent, CNNs

53 minute read

RNN building blocks, backpropagation through time, GRU, attention, convolutional alternatives.

59 minute read

Autoencoders, adjoint convolutions and pooling, variational autoencoders, generative adversarial nets

50 minute read

Markov decision process, optimal control, value-based learning, policy-based learning.

81 minute read

Toeplitz operators, manifolds, graphs, convolution, spectral CNN