Schedule

Lectures will be Mondays and Wednesdays 4:30pm - 6pm in 1670 Beyster.

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.

Since this is the first time this course has been taught at Michigan, the schedule is subject to change as we move through the semester.

Event	Date	Description	Course Materials
Lecture 1	Wednesday September 4	Course Introduction Computer vision overview Historical context Course logistics	[slides] [video] [Python tutorial] [GBC Sec 1.2] [GBC Sec 6.6]
Lecture 2	Monday September 9	Image Classification Data-driven approach K-Nearest Neighbor Hyperparameters Cross-validation	[slides] [video] [231n Image Classification]
Lecture 3	Wednesday September 11	Linear Classifiers Softmax / SVM classifiers L2 regularization	[slides] [video] [231n Linear Classification]
A1 Due	Sunday September 15	Assignment 1 Due PyTorch warmup kNN Classifier	[Assignment 1]
Lecture 4	Monday September 16	Optimization Stochastic Gradient Descent Momentum, AdaGrad, Adam Second-order optimizers	[slides] [video] [CS231n Optimization] [GBC Sec. 8.1 to 8.6]
Lecture 5	Wednesday September 18	Neural Networks Feature transforms Fully-connected networks Universal approximation Convexity	[slides] [video] [231n Neural Networks] [GBC Sec. 6.1 to 6.4] [Nielsen on Universal Approximation]
Lecture 6	Monday September 23	Backpropagation Computational Graphs Backpropagation Matrix multiplication example	[slides] [video] [231n Backpropagation] [Olah on Backprop] [Nielsen on Backprop]
Lecture 7	Wednesday September 25	Convolutional Networks Convolution Pooling Batch Normalization	[slides] [video] [CS231n ConvNets] [GBC Chapter 9]
Lecture 8	Monday September 30	CNN Architectures AlexNet, VGG, ResNet Size vs Accuracy Grouped and Separable Convolutions Neural Architecture Search	[slides] [video] [AlexNet] [VGG] [GoogLeNet] [ResNet]
A2 Due	Monday September 30	Assignment 2 Due Linear classifiers Two-layer network	[Assignment 2]
Lecture 9	Wednesday October 2	Hardware and Software CPUs, GPUs, TPUs Dynamic vs Static graphs PyTorch, TensorFlow	[slides] [video]
Lecture 10	Monday October 7	Training Neural Networks I Activation functions Data preprocessing Weight initialization Data augmentation Regularization (Dropout, etc)	[slides] [video] [CS231n Training I]
Lecture 11	Wednesday October 9	Training Neural Networks II Learning rate schedules Hyperparameter optimization Model ensembles Transfer learning Large-batch training	[slides] [video] [CS231n Training II] [Karpathy "Recipe for Training"]
	Monday October 14	No Class Fall Study Break
A3 Due	Monday October 14	Assignment 3 Due Modular API Convolutional Networks Batch Normalization Autograd	[Assignment 3]
Lecture 12	Wednesday October 16	Recurrent Networks RNN, LSTM, GRU Language modeling Sequence-to-sequence Image captioning Visual question answering	[slides] [video] [Karpathy "Unreasonable Effectiveness"]
Midterm	Monday October 21	In-class Midterm Exam Location: Chrysler 220 Time: 4:30pm - 6pm
Lecture 13	Wednesday October 23	Attention Multimodal attention Self-Attention Transformers	[slides] [video] [Bloem "Transformers from Scratch"] [Alammar "The Illustrated Transformer"]
Guest Lecture	Monday October 28	Guest Lecture: Luowei Zhou Vision and Language
Guest Lecture	Wednesday October 30	Guest Lecture: Prof. Atul Prakash Adversarial attacks
Lecture 14	Monday November 4	Visualizing and Understanding Feature visualization Adversarial examples DeepDream, Style transfer	[slides] [video] [Distill "Feature Visualization"] [Distill "Building Blocks of Interpretability"] [DeepDream] [neural-style]
Lecture 15	Wednesday November 6	Object Detection Single-stage detectors Two-stage detectors	[slides] [video]
Lecture 16	Monday November 11	Image Segmentation Semantic segmentation Instance segmentation Keypoint estimation	[slides] [video]
Lecture 17	Wednesday November 13	3D vision 3D shape representations Depth estimation 3D shape prediction Voxels, Pointclouds, SDFs, Meshes	[slides] [video]
A4 Due	Wednesday November 13	Assignment 4 Due RNNs, Attention Visualization, style transfer	[Assignment 4]
Lecture 18	Monday November 18	Videos Video classification Early / Late fusion 3D CNNs Two-stream networks	[slides] [video]
Lecture 19	Wednesday November 20	Generative Models I Supervised vs Unsupervised learning Discriminative vs Generative models Autoregressive models Variational Autoencoders	[slides] [video]
	Monday November 25	No class Thanksgiving break
	Wednesday November 27	No Class Thanksgiving break
Lecture 20	Monday December 2	Generative Models II More Variational Autoencoders Generative Adversarial Networks	[slides] [video]
Lecture 21	Wednesday December 4	Reinforcement Learning RL problem setup Bellman Equation Q-Learning Policy Gradient	[slides] [video]
Lecture 22	Monday December 9	Conclusion Course recap The future of computer vision	[slides] [video]
A5 due	Monday December 9	Assignment 5 Due Object detection	[Assignment 5]
A6 due	Tuesday December 17	Assignment 6 Due Variational Autoencoders Generative Adversarial Networks	[Assignment 6]