Fall 2016: Deep Learning (096260)
TA: Andrey Isakov
Hours: Sunday, 12:30-16:30
Simple networks, basic math and core algorithms: logistic regression, neural network, auto-encoders, non-convex optimization. Torch, Tensorflow, Theano.
More complex Networks: convolutional neural networks (VGG, AlexNet, Residual Net, FaceNet, GoogleNet), sequences (RNN, LSTM, GRU), Deep structured prediction.
Depth matters
Reinforcement learning (Playing Atari with Deep Reinforcement Learning)
The generative side of things: visualization, adversarial Networks, variational Auto-encoders
Generalization of deep networks
Spring 2016: Perturbation, Optimization and Statistics (096261)
TA: Alon Cohen
Hours: Sunday, 12:30-15:30
Selected chapters from the Perturbation, Optimization and Statistics book (NIPS workshop series, in press)