Lecture slides can be found here.

Lecture Feb. 4 overview and simple perceptron
Lecture Feb. 6 delta rule
Pset 0 optional Feb. 11 math review
Lecture Feb. 11 gradient optimization
Lecture Feb. 13 gradient learning
Pset 1 due Feb. 18 simple perceptrons
Lecture Feb. 18 multilayer perceptrons
Lecture Feb. 20 backpropagation
Pset 2 due Feb. 25 multilayer perceptrons
Lecture Feb. 25 training
Lecture Feb. 27 generalization
Pset 3 due Mar. 3 black art of backprop
Lecture Mar. 3 convolution and pooling
Lecture Mar. 5 object recognition, image-to-image
Lecture Mar. 10 biological vision
Lecture Mar. 12 class canceled
Spring break    
Lecture Mar. 24 biological vision
Pset 4 due Mar. 26 LeNet
Lecture Mar. 26 clustering and PCA as neural nets
Lecture Mar. 31 autoencoders
Midterm Apr. 2 midterm quiz
Lecture Apr. 7 n-grams and word embeddings
Pset 5 due Apr. 9 UNet
Lecture Apr. 9 RNNs, multistability
Lecture Apr. 14 RNNs, LSTMs, sequences
Pset 6 due Apr. 16 unsupervised learning
Lecture Apr. 16 seq2seq tasks
Homework 7 due Apr. 21 project proposal
Lecture Apr. 21 project meetings
Lecture Apr. 23 project meetings
Lecture Apr. 28 project meetings
Homework 8 due Apr. 30 intermediate report
Lecture May 7 final presentations
Homework 9 due May 12 final report due