Date	Topic	Reading	Items due
week 1 (Tue: 08/25)	Introduction to Computer Vision   Lecture Slides 1	Course logistics   Introduction to computer vision
week 1 (Thur: 08/27)	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 (Tue: 09/01)	Image Filtering: convolution   Lecture Slides 3     Notebook#1 (released on 08/31)
week 2 (Thur: 09/03)	Image-based Recognition   Image Classification and Neural Networks (NN)   Types of NN: Multilayer Perceptron (MLP)   Lecture Slides 4   Day04 Notes: PyTorch MLP	PyTorch Basics (review this before attempting activity#2)   PyTorch: nn.Linear()   PyTorch: nn.Sigmoid()   PyTorch: nn.Tanh()   PyTorch: nn.ReLU()
week 3 (Tue: 09/07)	Labor Day (university holiday; classes do not meet)		Notebook#1 (due on 09/07)
week 3 (Thur: 09/08)	Image-based Recognition   Image Classification and Neural Networks (NN)   Types of NN: Convolution Neural Network (CNN)   Lecture Slides 5   Day06 Notes: PyTorch CNN     Notebook #2 (released on 09/08)	PyTorch: nn.Conv2d()   PyTorch: nn.MaxPool2d()   PyTorch: nn.AvgPool2d()
week 4 (Tue: 09/15)	Popular Image Classification Networks (AlexNet, VGG, ResNet)   Dissecting Popular Pre-trained CNN models   Lecture Slides 6   Day07 Notes: AlexNet dissection   Day07 Notes: VGG-16 dissection   Day07 Notes: ResNet dissection	The Shattered Gradients Problem: If resnets are the answer, then what is the question?
week 4 (Thur: 09/17)	Fine-tuning a Pre-trained CNN model (AlexNet, VGG, ResNet)   Lecture Slides 7   Day08 Notes: Fine-tuning CNNs   Notebook#3 (Released on 09/16)		Notebook#2 (due on 09/16)
Week 5 (Tue: 09/22)	Vision Transformer (ViT)   Other ViT variants (SwinTransformer, MaxViT)   Lecture Slides 8   Day09 Notes: 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 5 (Thur: 09/24)	Object Detection: Classical and Deep Learing-Based   Lecture Slides 9a: Classical Detectors   Lecture Slides 9b: Deep learning-based Detectors (Faster R-CNN, Mask R-CNN)
week 6 (Tue: 09/29)	Inference with Popular Object Detectors (Faster R-CNN, Mask R-CNN)   Lecture Slides 10   Day11 Notes: Inference with Object Detector with Faster R-CNN   Quiz#1 (released on Blackboard 09/30)		Notebook#3 (due on 09/28)
week 6 (Thur: 10/01)	Lecture Slides 11   Fine-tuning Popular Object Detectors (Faster R-CNN, Mask R-CNN)   Day12 Notes: Fine-tuning Object Detectors (Detectron/Faster R-CNN)     Notebook#4: Fine-tuning Detectors: Faster & Mask R-CNN
week 7 (Tue: 10/06)	Image Segmentation   Classical Segmentation (Felzenswalb, SLIC, clustering)   Lecture Slides 12 part1   Lecture Slides 12 part2   Day13 Notes: Classical Image Segmentation Codes
week 7 (Thur: 10/08)	Vision Foundation Model for Image Segmentation: Segment Anything (SAM)   Lecture Slides 13 [pptx]   Day14 Notes: Inference with SAM: Generating Object Masks with SAM		Quiz#2 (due on 10/07)
week 8 (Tue: 10/13)	Fall Break (classes do not meet)
week 8 (Thur: 10/15)	Semantic Segmentaiton   Popular Semantic Segmentation Models (U-Net, FCN, SegNet, PSPNet)   Lecture Slides 14     Notebook #5: Semantic Segmentation with UNet		Notebook#4 (due on 10/14)
week 9 (Tue: 10/20)	Deep Neural Network Feature Space Visualization: tSNE   Application to Image Retrieval   Lecture Slides 15   Day17 Notes: Deep Features from Popular Networks (VGG, ResNet, ViT)
week 9 (Thur: 10/22)	Probability Basics   Discrete Probability Distribution   Lecture Slides 16 part1   Day18 Notes: Discrte probability distribution code   Continous Probability Distribution   Gaussian Distributions   Lecture Slides 16 part2   Day18 Notes: In-class activity#5: Continous probability distribution
week 10 (Tue: 10/27)	Image Generation and Deep Generative Models (Part#1)   Generative Adversarial Network (GAN)   Lecture Slides 17	AI Art	Notebook#5 (due on 10/26)
week 10 (Thur: 10/29)	Maximum Likelihood (ML) Estimate   Lecture Slides 18 part1   Day20 Notes: In-class activity#6: Maximum likelihood estimate for Gaussian distribution   Quiz#2 (released on 10/28)	Tutorial on variational autoencoder by Carl Doersch   Generating Counterfactual Images: C2C-VAE - CBREIS'22   Generative model: implicit reparameterization   Wasserstein Distance
Week 11 (Tue: 11/03)	Image Generation and Deep Generative Models (Part#2)   Variational Auto Encoder (VAE)   Lecture Slides 19   Day21 Notes: In-class activity#7: VAE code for digit image generation using MNIST
Week 11 (Thur: 11/05)	Image Generation and Deep Generative Models (Part#3)   Diffusion Model   Lecture Slidees 20   Day22 Notes: In-class activity#8: Image generation from text using Stable Diffusion	Diffusion models are evolutionary algorithms - arxiv'24	Quiz#2 (due on 11/04)
Week 12 (Tue: 11/10)	Image Transformation   Linear transformation   Lecture Slidees 21   In-class activity#8: Rotation, Scaling, Shearing transformation
Week 12 (Thur: 11/12)
Week 13 (Tue: 11/17)	Homogenous coordinates   Affine and projective transformation   Lecture Slides 22a     Camera Projection   Modeling projection   Activity: projection practice   Generalizing the model   Adding a lens   Lecture Slides 22b	From Images to 3D Models-ACM Communication'2002
Week 13 (Thur: 11/19)	Classical 3D Reconstruction (Part#1)   Tueocular Depth Estimation: 3D Reconstruction from Single Images   Stereo Matching: 3D Reconstruction from Two Images   Multiview Stereo: 3D Reconstruction from Multiple Images   Lecture Slides		Final Project Presentation on 12/11
Week 14 (Tue: 11/24)	Deep Learning-based 3D Reconstruction (Part#2)   Monocular Depth Estimation Demo   Stereo Matching Demo   Multiview Stereo Demo   Lecture Slides
week 14 (Thur: 11/26)	Thanksgiving Break (classes do not meet)
Week 15 (Tue: 12/01)	Large Multimodal Model (LMM)   LMM Application using PyTorch   Lecture Slides 27	Reading:   Dissociating language and thought in large language models-2024   Is OpenAI-o1 reasoning? (ML Street Talk'24)   World Model (Jurgen Schmidhuber@NeurIPS'18)   LoRA: Low-Rank Adaptation of Large Language Models (ICLR'22)
Week 15 (Thur: 12/03)	Final project group presentation   Location: Collier-Scripps#335   Quiz#3 (released on 12/03)   Course Evaluation
Week 16 (Tueday: 12/07)	No final exam!	Reading:	Q3 (due on 12/07)   Final Project Code +   Presentation Slides (due by 12/11)