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   AlexNet dissection   VGG-16 dissection   ResNet dissection		Notebook#2 (due on 09/23)
Week 5 (Wed: 09/25)	Fine-tuning a Pre-trained CNN model   Lecture Slides 9   Fine-tuning CNNs   Notebook#3 (Fine-tuning popular CNNs)
Week 6 (Mon: 09/30)	Vision Transformer (ViT)   Other ViT variants (Swin Transformer Tiny)   ConvNext, Inflated-ConvNext Tiny   Lecture Slides 10   Vision Transformer (ViT) Dissection	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 and Deep Learing-Based   Lecture Slides 11a: Classical Detectors   Lecture Slides 11b: Deep learning-based Detectors (Faster R-CNN, Mask R-CNN)		Notebook#3 (due on 10/02)
Week 7 (Mon: 10/07)	Inference with Popular Object Detectors (Faster R-CNN, Mask R-CNN)   Lecture Slides 12   Inference with Object Detector: Faster R-CNN
Week 7 (Wed: 10/09)	Lecture Slides 13   Fine-tuning Popular Object Detectors (Faster R-CNN, Mask R-CNN)   Fine-tuning Object Detector: Faster R-CNN   Quiz#1 (released on 10/09)
Week 8 (Mon: 10/14)	Fall Break (no class)
Week 8 (Wed: 10/16)	Image Segmentation   Classical Segmentation (Felzenswalb, SLIC, clustering)   Lecture Slides 13 part1   Lecture Slides 13 part2   Classical Image Segmentation Codes		Quiz#1 (due on 10/16)
Week 9 (Mon: 10/21)	Vision Foundation Model for Image Segmentation: Segment Anything (SAM)   Lecture Slides 14 [pptx]
Week 9 (Wed: 10/23)	Inference with SAM: Automatically generating object masks with Segment Anything (SAM)   Semantic Segmentaiton   Popular Semantic Segmentation Models (FCN, SegNet, U-Net, PSPNet)   Lecture Slides 15
Week 10 (Mon: 10/28)	Probability Basics   Discrete Probability Distribution   Lecture Slides 16 part1   Discrte probability distribution code   Continous Probability Distribution   Gaussian Distributions   Lecture Slides 16 part2   In-class activity#5: Continous probability distribution     Notebook#4 (Fine-tuning Detectors: Faster & Mask R-CNN)
Week 10 (Wed: 10/30)	Classes do not meet in-person   Dr. Reza is traveling to China as part of a Drake University delegation
Week 11 (Mon: 11/04)	Classes do not meet in-person   Dr. Reza is traveling to China as part of a Drake University delegation
Week 11 (Wed: 11/06)	Image Generation and Deep Generative Models: Generative Adversarial Network (GAN)   Lecture Slides 17
Week 12 (Mon: 11/11)	Maximum Likelihood (ML) Estimate   Lecture Slides 18 part1   In-class activity#6: Maximum likelihood estimate for Gaussian distribution   Image Generation and Deep Generative Models: Variational Auto Encoder (VAE)   Lecture Slides 18 part2	Tutorial on variational autoencoder by Carl Doersch   Generating Counterfactual Images: C2C-VAE - CBREIS'22   Generative model: implicit reparameterization   Wasserstein Distance
Week 12 (Wed: 11/13)	Image Generation and Deep Generative Models: Variational Auto Encoder (VAE)   Lecture Slides 18 part2 (continued)   In-class activity#7: VAE code for digit image generation using MNIST dataset   Quiz#2 (released on 11/13)		Notebook#4 (due on 11/13)
Week 13 (Mon: 11/18)	Image Generation and Deep Generative Models: Diffusion Model   Lecture Slidees 19   In-class activity#8: Generating images from text prompt using Stable Diffusion Model	Diffusion models are evolutionary algorithms - arxiv'24	Quiz#2 (due on 11/19)
Week 13 (Wed: 11/20)	Image Transformation   Linear transformation   Lecture Slidees 20   In-class activity#8: Rotation, Scaling, Shearing transformation
Week 14 (Mon: 11/25)	Notebook #5 (Semantic Segmentation Inference and Training)	From Images to 3D Models-ACM Communication'2002
Week 14 (Wed: 11/27)	Thanksgiving Break (no class)
Week 15 (Mon: 12/02)	Homogenous coordinates   Affine and projective transformation   Lecture Slides 21 part1     Camera Projection   Modeling projection   Activity: projection practice   Generalizing the model   Adding a lens   Lecture Slides 21 part2
Week 15 (Wed: 12/04)	3D Reconstruction from Images: Stereo Matching   Depth from stereo   The case of parallel cameras   Parallel cameras and rectification   Naive stereo matching   Stereo matching with deep neural network   Lecture Slides		Notebook#5 (due on 12/04)   Final Project Presentation on 12/11
Week 16 (Mon: 12/09)	Quiz#3 (released on 12/10)   No final exam!
Week 16 (Wed: 12/11)	Final project group presentation   Course Evaluation   @Collier-Scripps#335		Q3 (due on 12/13)   Final Project Code +   Presentation Slides (due by 12/16)