| Lecture | Feb. 6 | overview and simple perceptron | |
| Lecture | Feb. 8 | delta rule | |
| Pset 1 due | Feb. 13 | simple perceptrons | |
| Lecture | Feb. 13 | multilayer perceptrons | |
| Lecture | Feb. 15 | backpropagation | |
| Pset 2 due | Feb. 20 | multilayer perceptrons | |
| Lecture | Feb. 20 | stochastic gradient descent | |
| Lecture | Feb. 22 | generalization and regularization | |
| Pset 3 due | Feb. 27 | black art of backprop | |
| Lecture | Feb. 27 | convolution and pooling | |
| Lecture | Mar. 1 | ConvNet backprop | |
| Pset 4 due | Mar. 6 | LeNet | |
| Lecture | Mar. 6 | contemporary ConvNets | |
| Lecture | Mar. 8 | midterm review | |
| Lecture | Mar. 13 | biological vision | |
| Midterm | Mar. 15 | in-class exam | |
| Spring break | |||
| Lecture | Mar. 27 | deep learning frameworks | |
| Lecture | Mar. 29 | parallel and distributed algorithms | |
| Pset 5 due | Apr. 3 | ConvNets at scale | |
| Lecture | Apr. 3 | competitive learning and clustering | |
| Lecture | Apr. 5 | PCA and autoencoders | |
| Pset 6 due | Apr. 10 | unsupervised learning | |
| Lecture | Apr. 10 | contrastive Hebbian learning | |
| Lecture | Apr. 12 | MLPs for language | |
| Pset 7 due | Apr. 17 | language modeling | |
| Lecture | Apr. 17 | backprop through time | |
| Lecture | Apr. 19 | RNNs for language | |
| Pset 8 due | Apr. 24 | recurrent nets | |
| Lecture | Apr. 24 | policy gradient | |
| Lecture | Apr. 26 | value function | |
| Pset 9 due | May 1 | reinforcement learning | |
| Lecture | May 1 | unsupervised learning in the brain | |
| Lecture | May 3 | final review | |
| Final | May 22 | final exam |