Date	Topics	Readings	Items due
Week 1 (Mon: 08/26)	Introduction to Computer Vision   Lecture Slides 1	Course logistics   Introduction to computer vision
Week 1 (Wed: 08/28)	Image Filtering: cross-correlation   Lecture Slides 2   In-class activity #1 (image filtering cross-correlation)	Pillow concepts   Pillow tutorial   Image.open(path)   Image.save(path)   Image.new('L', (width, height), color=0)   Image.getpixel( (x,y) )   Image.putpixel( (x,y), value)   ImageDraw( img )
Week 2 (Wed: 09/02)	Labor Day (university holiday; classes do not meet)
Week 2 (Wed: 09/04)	Image Filtering: convolution   Lecture Slides 3   Activity (image filtering: convolution)   Notebook #1 (released on 09/05)	Image.histogram()
Week 3 (Mon: 09/09)	Image Recognition   Image Classification and Neural Networks (NN)   Types of NN: Multilayer Perceptron (MLP)   Lecture Slides 4   In-class activity #2 (PyTorch MLP)	PyTorch Basics (review this before attempting activity#2)   PyTorch: nn.Linear()   PyTorch: nn.Sigmoid()   PyTorch: nn.Tanh()   PyTorch: nn.ReLU()
Week 3 (Wed: 09/11)	In-class activity #2 (PyTorch MLP) continued   Types of NN: Convolution Neural Network (CNN)   Lecture Slides 5	PyTorch: nn.Conv2d()   PyTorch: nn.MaxPool2d()   PyTorch: nn.AvgPool2d()	Notebook#1 (due on 09/13)
Week 4 (Mon: 09/16)	Reza is traveling (class does not meet)   Midwest Computer Vision Workshop@Indiana University Bloomington   Notebook #2 (released on 09/16)
Week 4 (Wed: 09/18)	In-class activity #3 (PyTorch CNN)   Notebook#2 discussion
Week 5 (Mon: 09/23)	Popular Image Classification Networks (AlexNet, VGG, ResNet)   Dissecting Popular Pre-trained CNN models   Lecture Slides 8		Notebook#2 (due on 09/23)
Week 5 (Wed: 09/25)	Fine-tuning a Pre-trained CNN model   Lecture Slides 9   Notebook #3 (Fine-tuning popular CNNs)   Quiz#1 (will be released on 09/26)
Week 6 (Mon: 09/30)	Vision Transformer (ViT)   Swin Transformer Tiny   ConvNext   Inflated-ConvNext Tiny   Lecture Slides 10	The Hardware Lottery - Sara Hooker   Transformer (designed to win the hardware lottery)   Vision Transformer (ViT): Transformers for image recognition at scale
Week 6 (Wed: 10/02)	Object Detection   Classical Object Detection   Popular Object Detectors (Mask R-CNN, YOLO)   Lecture Slides 11   In-class activity #4 (Inference with Popular Detectors)		Notebook#3 (due on 10/02)   Quiz#1 (due on 10/03)
Week 7 (Mon: 10/07)	Fine-tuning Popular Object Detectors (Mask R-CNN, YOLO)   Lecture Slides 12   Notebook #4 (Finetuning Popular Detectors)
Week 7 (Wed: 10/09)	Image Segmentation   Classical Segmentation (Felzenswalb, SLIC, clustering)   Vision Foundation Model for Image Segmentation: Segment Anything (SAM)   In-class activity #5 (Segmentation with SAMv1 and SAMv2)   Lecture Slides 13
Week 8 (Wed: 10/14)	Fall Break (no class)		Notebook#4 (due on 09/14)
Week 8 (Mon: 10/16)	Semantic Segmentaiton   Popular Semantic Segmentation Models (FCN-ResNet)   Lecture Slides 14   In-class activity #6 (Inference with Popular Semantic Segmentation Models)   Semantic Segmentaiton   Training Popular Semantic Segmentation Models (FCN-ResNet)   Lecture Slides 15   Notebook #5
Week 9 (Mon: 10/21)	Probability Basics   Discrete Probability Distribution   Lecture Slides 16   Continous Probability Distribution   Gaussian Distributions   Lecture Slides 17   In-class activity #7 (Exploring Gaussian distribution)
Week 9 (Wed: 10/23)	Maximum Likelihood (ML) Estimate   Lecture Slides 18   In-class activity #8 (Maximum likelihood estimate for Gaussian distribution)
Week 10 (Mon: 10/28)	Image Generation   Deep Generative Models: Generative Adversarial Network (GAN)   Lecture Slides 19   In-class activity #9 (Image to Image Translation with GAN)
Week 10 (Wed: 10/30)	Image Generation   Deep Generative Models: Variational Auto Encoder (VAE)   Lecture Slides 20	Tutorial on variational autoencoder by Carl Doersch   Generative model: implicit reparameterization   Wasserstein Distance	Q2 (due by 10/30)
Week 11 (Mon: 11/04)	Deep Generative Models (Diffusion Model)   Diffusion Model for Image Generation   Diffusion Model for Video Generation (text2video, OpenAI Sora, Gen2, VideoCrafter, PikaLab)   Lecture Slides 21
Week 11 (Wed: 11/06)	Transformation   Image warping   Activity: Warping   Linear transformation   Homogenous coordinates   Affine and projective transformation   Lecture Slides 22
Week 12 (Mon: 11/11)	Camera Projection   Modeling projection   Activity: projection practice   Generalizing the model   Adding a lens   Lecture Slides 23
Week 12 (Wed: 11/13)	Feature points   Scale Invariant Feature Transform (SIFT)   Image and feature matching   Activity: applying SIFT on real images   Deep feature learning with Siamese Neural Network (Reza AIPR'20)   Frozen deep features for different tasks (eg, crowd counting): DINO, SAM, CLIP   Lecture Slides 24
Week 13 (Mon: 11/18)	Image Matching   RANdom SAmple Consensus (RANSAC)   Activity: how many rounds?   RANSAC parameters and implementation details   Lecture Slides 25
Week 13 (Wed: 11/20)	Stereo   Depth from stereo   The case of parallel cameras   Parallel cameras and rectification   Naive stereo matching   Stereo matching with deep neural network   Lecture Slides 26
Week 14 (Mon: 11/25)	3D Reconstruction from Images   Structure from Motion (SfM)   Lecture Slides 27	From Images to 3D Models-ACM Communication'2002
Week 14 (Wed: 11/27)	Thanksgiving Break (no class)
Week 15 (Mon: 12/02)	3D Reconstruction from Images   Deep Learning Based 3D Reconstruction   Lecture Slides 28
Week 15 (Wed: 12/04)	Final project presentation   Lecture Slides 29   Course Evaluation
Week 16 (Mon: 12/09)	No final exam!		Q3 (due on 12/11)   Final Project Code + Report (due by 12/11)