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)
• Deep Learning (DL)
• Robotics (RB)
• Convex Optimization (CO)

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
❌	Not started
🔶	In progress
✅	Finished

Computer vision

Chapter Title	Progress
Introduction to Computer Vision	❌
Camera Geometry	❌
Camera Calibration	✅
Image Processing	✅
Edge Detection	✅
Image Restoration	❌
Radiometry and Color	❌
Stereopsis	❌
Two-view Geometry	✅
...	❌

Deep Learning

Chapter Title	Progress
Introduction	✅
Automatic differentiation	🔶
Deep Reinforcement Learning	🔶
Optimization and loss functions	✅
Convolutional Neural Networks	✅
Recurrent Neural Networks	✅
Attention and Transformers	✅
Robustness and Regularity	✅
Generative and Autoregressive Models	✅
Autoencoders	✅
Generative Adversarial Neural Networks	✅
Normalizing Flows	✅

Robotics

Chapter Title	Progress
Introduction	❌
Position and Orientation	✅
Forward Kinematics	✅
Inverse Kinematics	✅
Direct and Inverse Dynamics	✅
Motion Planning	✅
Collision Detection	🔶
Optimal Control	❌

Convex Optimization

Chapter Title	Progress
Introduction	✅
Convex sets	✅
Convex functions	✅
Convex problems	✅
Duality I	✅
Duality II	✅
Base methods for unconstrained optimization	❌
Constrained optimization	❌
Splitting methods and monotone operators	❌
Stochastic methods	❌

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.