Schedule and Syllabus

(The syllabus for the (previous) Winter 2015 class offering has been moved here.)
Unless otherwise specified the course lectures and meeting times are Monday, Wednesday 3:00-4:20, Bishop Auditorium in Lathrop Building (map)

Update: The class has ended! There are many people to thank for making this class run smoothly: Andrej Karpathy for the class notes and lectures, Justin Johnson the assignments and lectures, Fei-Fei Li for maintaining order, the entire TA team for their hard work on grading, office hours, and class logistics, and our wonderful students for their valuable feedback! The final course projects were posted here. You can find the raw lecture slides (Google Presentations) here and feel free to use material from any of the slides. Stay in touch on Twitter or Reddit r/cs231n, and we'll see you again next year!
Update2: We are working hard to bring the videos back up. Sorry about that and stay tuned.
Event TypeDateDescriptionCourse Materials
Lecture Jan 4 Intro to Computer Vision, historical context. [slides]
Lecture Jan 6 Image classification and the data-driven approach
k-nearest neighbor
Linear classification I
[slides] [video]
[python/numpy tutorial]
[image classification notes]
[linear classification notes]
Lecture Jan 11 Linear classification II
Higher-level representations, image features
Optimization, stochastic gradient descent
[slides] [video]
[linear classification notes]
[optimization notes]
Lecture Jan 13 Backpropagation
Introduction to neural networks
[slides] [video]
[backprop notes]
[Efficient BackProp] (optional)
related: [1], [2], [3] (optional)
Lecture Jan 18 Holiday; No class.
A1 Due Jan 20 Assignment #1 (kNN/SVM/Softmax/2-Layer Net) Due date [Assignment #1]
Lecture Jan 20 Training Neural Networks Part 1
activation functions, weight initialization, gradient flow, batch normalization
babysitting the learning process, hyperparameter optimization
[slides] [video]
Neural Nets notes 1
Neural Nets notes 2
Neural Nets notes 3
tips/tricks: [1], [2], [3] (optional)
Deep Learning [Nature] (optional)
Lecture Jan 25 Training Neural Networks Part 2: parameter updates, ensembles, dropout
Convolutional Neural Networks: intro
[slides] [video]
Neural Nets notes 3
Lecture Jan 27 Convolutional Neural Networks: architectures, convolution / pooling layers
Case study of ImageNet challenge winning ConvNets
[slides] [video]
ConvNet notes
Proposal due Jan 30 Couse Project Proposal due [proposal description]
Lecture Feb 1 ConvNets for spatial localization
Object detection
[slides] [video]
Lecture Feb 3 Understanding and visualizing Convolutional Neural Networks
Backprop into image: Visualizations, deep dream, artistic style transfer
Adversarial fooling examples
[slides] [video]
A2 Due Feb 5 Assignment #2 (Neural Nets) Due date [Assignment #2]
Lecture Feb 8 Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM)
RNN language models
Image captioning
[slides] [video]
DL book RNN chapter (optional)
min-char-rnn, char-rnn, neuraltalk2
Midterm Feb 10 In-class midterm
Lecture Feb 15 Holiday; No class.
Milestone Feb 17 Course Project Milestone
Lecture Feb 17 Training ConvNets in practice
Data augmentation, transfer learning
Distributed training, CPU/GPU bottlenecks
Efficient convolutions
[slides] [video]
Lecture Feb 22 Overview of Caffe/Torch/Theano/TensorFlow [slides] [video]
A3 Due Feb 24 Assignment #3 (ConvNets) Due date [Assignment #3]
Lecture Feb 24 Segmentation
Soft attention models
Spatial transformer networks
[slides] [video]
Lecture Feb 29 ConvNets for videos
Unsupervised learning
[slides] [video]
Lecture Mar 2 Invited Talk: Jeff Dean [video]
Lecture Mar 7 Student spotlight talks, conclusions [slides]
Poster Presentation Mar 9
Final Project Due Mar 13 Final course project due date [reports]