> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Computer Vision Course Syllabus

> Syllabus for AI for Computer Vision course.

## Books

1. **TIF** - [Foundations of Computer Vision](https://visionbook.mit.edu/) by Antonio Torralba, Phillip Isola and William T. Freeman. Free online. Covers the latest deep learning applications including diffusion models.

2. **BISHOP** - [Deep Learning - Foundations and Concepts](https://www.bishopbook.com/) by C. Bishop and H. Bishop. Available to view online from the book's website.

3. **SZELINSKI** - [Computer Vision: Algorithms and Applications](https://szeliski.org/Book/), 2nd Edition. Free to [download](https://szeliski.org/Book/download.php) for personal use. Alternative to TIF for some topics.

## Planned Schedule

### Part I: Detection and Segmentation

| Lecture | Topic                 | Description                                                                                                                                                      |
| ------- | --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **1**   | Introduction          | Computer vision for agents with egomotion. Prerequisites review: Python, linear algebra, probability theory, camera fundamentals. **Reading:** TIF Chapter 1     |
| **2**   | Statistical Learning  | End-to-end prediction, featurization, fully connected neural architectures, maximum likelihood optimization. **Reading:** TIF Chapters 9-10, BISHOP Chapters 4-5 |
| **3**   | Dense Neural Networks | Cross entropy loss, training and regularization of dense layers. **Reading:** TIF Chapters 12-13, BISHOP Chapter 6                                               |
| **4**   | CNNs                  | Spatial feature hierarchies, image classification, ResNets for real-time perception. **Reading:** TIF Chapter 24, BISHOP Chapter 10                              |
| **5**   | Object Detection      | YOLO and Faster R-CNN architectures for identifying and locating objects. **Reading:** TIF Chapter 50                                                            |
| **6**   | Semantic Segmentation | Pixel-level labeling, panoptic segmentation for full scene understanding. **Reading:** SZELINSKI Chapter 6                                                       |
| **7**   | Vision Transformers   | Self-attention for global image dependencies, ViT vs CNN trade-offs. **Reading:** BISHOP Chapter 12, TIF Chapter 26                                              |
| **8**   | Object Tracking       | Video stream processing, handling occlusion, motion blur, appearance changes. **Reading:** TIF Chapter 5                                                         |

### Part II: Vision Language Models (VLMs)

| Lecture | Topic                        | Description                                                                                             |
| ------- | ---------------------------- | ------------------------------------------------------------------------------------------------------- |
| **9**   | Contrastive Learning         | Vision-language pretraining, CLIP for relating images and text. **Reading:** CLIP paper, TIF Chapter 51 |
| **10**  | From Retrieval to Generation | BLIP-2, LLaVA for image captioning and Visual Question Answering.                                       |
| **11**  | Prompted Vision Models       | Meta's SAM as a worker receiving multimodal prompts from VLM planners.                                  |

### Part III: Generative Vision Models

| Lecture | Topic                  | Description                                                                                                            |
| ------- | ---------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| **12**  | Neural Radiance Fields | NeRF for creating 3D scenes from 2D images, volume rendering concepts. **Reading:** TIF Chapter 45                     |
| **13**  | Diffusion Models       | Physics-inspired learning, conditional image generation, DALL-E and Stable Diffusion. **Reading:** TIF Chapters 32, 34 |

***

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