Lecture slides can be found here.

Lecture Feb. 5 overview and simple perceptron
Lecture Feb. 7 delta rule
Pset 0 optional Feb. 12 math review
Lecture Feb. 12 stochastic gradient descent
Lecture Feb. 14 multilayer perceptrons
Pset 1 due Feb. 19 simple perceptrons
Lecture Feb. 19 backpropagation
Lecture Feb. 21 generalization and regularization
Pset 2 due Feb. 26 multilayer perceptrons
Lecture Feb. 26 convolution and pooling
Lecture Feb. 28 ConvNet backprop
Pset 3 due Mar. 5 black art of backprop
Lecture Mar. 5 visual object recognition
Lecture Mar. 7 biological vision
Pset 4 due Mar. 12 LeNet
Lecture Mar. 12 no class (instructor was sick)
Lecture Mar. 14 image-to-image computations
Spring break    
Midterm Mar. 26 midterm exam
Lecture Mar. 28 competitive learning and clustering
Lecture Apr. 2 principal component analysis
Lecture Apr. 4 autoencoders and siamese nets
Pset 5 due Apr. 9 dense prediction on images
Lecture Apr. 9 n-grams and word embeddings
Lecture Apr. 11 RNNs and LSTMs for language
Pset 6 due Apr. 16 unsupervised learning
Lecture Apr. 16 backprop through time
Lecture Apr. 18 "Teaching a car to drive" (Larry Jackel)
Pset 7 due Apr. 23 word embeddings
Lecture Apr. 23 policy gradient
Lecture Apr. 25 Markov decision processes
Pset 8 due Apr. 30 language modeling
Lecture Apr. 30 value iteration
Lecture May 2 final review
Pset 9 due May 7 reinforcement learning
Final TBA final exam

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