https://github.com/red-rapious/cs-classes-m1-s1
My Computer Science classes for the first semester (S1) of second year (M1) at ENS Ulm.
https://github.com/red-rapious/cs-classes-m1-s1
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My Computer Science classes for the first semester (S1) of second year (M1) at ENS Ulm.
- Host: GitHub
- URL: https://github.com/red-rapious/cs-classes-m1-s1
- Owner: Red-Rapious
- Created: 2024-05-25T10:13:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-19T21:30:36.000Z (about 1 year ago)
- Last Synced: 2025-01-11T19:44:27.495Z (about 1 year ago)
- Topics: classes, course
- Language: TeX
- Homepage:
- Size: 164 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CS-Classes-M1-S1
The class notes of my computer science classes for the first semester of second year (M1) at ENS Ulm.
## Content
Available classes are:
- [Introduction to Computer Vision (CV)](computer-vision/computer-vision.pdf)
- [Deep Learning (DL)](deep-learning/deep-learning.pdf)
- [Robotics (RB)](robotics/robotics.pdf)
- [Convex Optimization (CO)](convex-optimization/convex-optimization.pdf)
## Progression
Classes are in active development. Below is a summary of the current availability of chapters. Note that chapter titles for future lectures are subject to modifications.
*The following legend is used:*
| ***Symbol*** | ***Meaning*** |
|------------|-------------|
| :x: | *Not started* |
| :large_orange_diamond: | *In progress* |
| :white_check_mark: | *Finished* |
### Computer vision
| **Chapter Title** | **Progress** |
|-------------------|--------------|
| Introduction to Computer Vision | :x: |
| Camera Geometry | :x: |
| Camera Calibration | :white_check_mark: |
| Image Processing | :white_check_mark: |
| Edge Detection | :white_check_mark: |
| Image Restoration | :x: |
| Radiometry and Color | :x: |
| Stereopsis | :x: |
| Two-view Geometry | :white_check_mark: |
| ... | :x: |
### Deep Learning
| **Chapter Title** | **Progress** |
|-------------------|--------------|
| Introduction | :white_check_mark: |
| Automatic differentiation | :large_orange_diamond: |
| Deep Reinforcement Learning | :large_orange_diamond: |
| Optimization and loss functions | :white_check_mark: |
| Convolutional Neural Networks | :white_check_mark: |
| Recurrent Neural Networks | :white_check_mark: |
| Attention and Transformers | :white_check_mark: |
| Robustness and Regularity | :white_check_mark: |
| Generative and Autoregressive Models | :white_check_mark: |
| Autoencoders | :white_check_mark: |
| Generative Adversarial Neural Networks | :white_check_mark: |
| Normalizing Flows | :white_check_mark: |
### Robotics
| **Chapter Title** | **Progress** |
|-------------------|--------------|
| Introduction | :x: |
| Position and Orientation | :white_check_mark: |
| Forward Kinematics | :white_check_mark: |
| Inverse Kinematics | :white_check_mark: |
| Direct and Inverse Dynamics | :white_check_mark: |
| Motion Planning | :white_check_mark: |
| Collision Detection | :large_orange_diamond: |
| Optimal Control | :x: |
### Convex Optimization
| **Chapter Title** | **Progress** |
|-------------------|--------------|
| Introduction | :white_check_mark: |
| Convex sets | :white_check_mark: |
| Convex functions | :white_check_mark: |
| Convex problems | :white_check_mark: |
| Duality I | :white_check_mark: |
| Duality II | :white_check_mark: |
| Base methods for unconstrained optimization | :x: |
| Constrained optimization | :x: |
| Splitting methods and monotone operators | :x: |
| Stochastic methods | :x: |
## Contribution
Contributing to this repository is encouraged: please let me know about typos and suggestions, using the GitHub issue feature or through a PR. Small additions are also welcomed.
## Disclaimer
Despite being written and organized by me, these documents contain material heavily inspired by my teacher's own classes.