Schedule

Lectures will be Mondays and Wednesdays 1:30 - 3pm on Zoom. Attendance is not required. Recordings will be posted after each lecture in case you are unable the attend the scheduled time.

Some lectures have reading drawn from the course notes of Stanford CS 231n, written by Andrej Karpathy.

Some lectures have optional reading from the book Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (GBC for short). The entire text of the book is available for free online so you don’t need to buy a copy.

Event	Date	Description	Course Materials
Lecture 1	Monday August 31	Course Introduction Computer vision overview Historical context Course logistics	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [Python tutorial] [GBC Sec 1.2] [GBC Sec 6.6]
Lecture 2	Wednesday September 2	Image Classification Data-driven approach K-Nearest Neighbor Hyperparameters Cross-validation	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [231n Image Classification]
	Monday September 7	No class Labor Day
Lecture 3	Wednesday September 9	Linear Classifiers Algebraic / Visual / Geometric viewpoints Softmax / SVM classifiers	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [231n Linear Classification]
A1 Due	Friday September 11	Assignment 1 Due PyTorch warmup kNN Classifier	[Assignment 1]
Lecture 4	Monday September 14	Regularization + Optimization Regularization Weight decay Stochastic Gradient Descent Momentum, AdaGrad, Adam Second-order optimizers	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [CS231n Optimization] [GBC Sec. 8.1 to 8.6]
Lecture 5	Wednesday September 16	Neural Networks Feature transforms Fully-connected networks Universal approximation Convexity	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [231n Neural Networks] [GBC Sec. 6.1 to 6.4] [Nielsen on Universal Approximation]
Lecture 6	Monday September 21	Backpropagation Computational Graphs Backpropagation Matrix multiplication example	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [Backprop for matrix multiply] [231n Backpropagation] [Olah on Backprop] [Nielsen on Backprop]
Lecture 7	Wednesday September 23	Convolutional Networks Convolution Pooling Batch Normalization	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [CS231n ConvNets] [GBC Chapter 9]
A2 Due	Friday September 25	Assignment 2 Due Linear classifiers Two-layer network	[Assignment 2]
Lecture 8	Monday September 28	CNN Architectures AlexNet, VGG, ResNet Size vs Accuracy Grouped and Separable Convolutions Neural Architecture Search	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [AlexNet] [VGG] [GoogLeNet] [ResNet]
Lecture 9	Wednesday September 30	Hardware and Software CPUs, GPUs, TPUs Dynamic vs Static graphs PyTorch, TensorFlow	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Lecture 10	Monday October 5	Training Neural Networks I Activation functions Data preprocessing Weight initialization Data augmentation Regularization (Dropout, etc)	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [CS231n Training I]
Lecture 11	Wednesday October 7	Training Neural Networks II Learning rate schedules Hyperparameter optimization Model ensembles Transfer learning Large-batch training	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [CS231n Training II] [Karpathy "Recipe for Training"]
A3 Due	Friday October 9	Assignment 3 Due Modular API Convolutional Networks Batch Normalization Autograd	[Assignment 3]
Lecture 12	Monday October 12	Recurrent Networks RNN, LSTM, GRU Language modeling Sequence-to-sequence Image captioning Visual question answering	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [Karpathy "Unreasonable Effectiveness"]
Lecture 13	Wednesday October 14	Attention Multimodal attention Self-Attention Transformers	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [Bloem "Transformers from Scratch"] [Alammar "The Illustrated Transformer"]
Midterm	Monday October 19	Midterm Exam Details TBD
Lecture 14	Wednesday October 21	Visualizing and Understanding Feature visualization Adversarial examples DeepDream, Style transfer	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [Distill "Feature Visualization"] [Distill "Building Blocks of Interpretability"] [DeepDream] [neural-style]
Lecture 15	Monday October 26	Object Detection Single-stage detectors Two-stage detectors	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Lecture 16	Wednesday October 28	Image Segmentation Semantic segmentation Instance segmentation Keypoint estimation	[slides] [FA2020 video (UMich only)] [FA2019 video (public)] [Girshick, Object Detection as a ML Problem]
A4 Due	Friday October 30	Assignment 4 Due RNNs, Attention Visualization, style transfer	[Assignment 4]
Guest Lecture	Monday November 2	Guest Lecture: Timnit Gebru Computer Vision: who benefits and who is harmed?	[video (UMich only)]
Lecture 17	Wednesday November 4	3D vision 3D shape representations Depth estimation 3D shape prediction Voxels, Pointclouds, SDFs, Meshes	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Lecture 18	Monday November 9	Videos Video classification Early / Late fusion 3D CNNs Two-stream networks	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Lecture 19	Wednesday November 11	Generative Models I Supervised vs Unsupervised learning Discriminative vs Generative models Autoregressive models Variational Autoencoders	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Lecture 20	Monday November 16	Generative Models II More Variational Autoencoders Generative Adversarial Networks	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Guest Lecture	Wednesday November 18	Guest Lecture: Andrej Karpathy
A5 due	Monday Wednesday 18	Assignment 5 Due Object detection	[Assignment 5]
	Monday November 23	No class Thanksgiving break
	Wednesday November 25	No Class Thanksgiving break
Lecture 21	Monday November 30	Reinforcement Learning RL problem setup Bellman Equation Q-Learning Policy Gradient	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
Lecture 22	Wednesday December 2	Conclusion Course recap The future of computer vision	[slides] [FA2020 video (UMich only)] [FA2019 video (public)]
A6 due	Wednesday December 9	Assignment 6 Due Variational Autoencoders Generative Adversarial Networks	[Assignment 6]