## Current Lectures

- Deep learning
- Linear and logistic regressions and their probabilistic interpretations
- Multi-layer perceptrons
- Backpropagation, stochastic gradient
- Convolutional layers and networks, application to images
- Classical, variational and convolutional autoencoders
- GAN and others friends (generative models)
- Transformers for text
- Physical Informed Neural Networks (PINNs)
- Tools around: pytorch, autograd, lightning, tensorboard

- Introduction to programming (in Python) in the first year
- Scientific computing with Python
- Python intermediate level
- Libraries: numpy, scipy, matplotlib, pandas
- Object programmming in Python
- Interactive notebooks

- Engineering tools for scientific computing
- The Linux system: tree structure, remote execution
- Code management with git
- Data processing: pandas
- Task orchestration with luigi

- Array algorithms
- Introduction to algorithms and the notion of complexity
- Internal representation of lists in Python
- Sorting algorithms
- Introduction to recursion
- Selection algorithms

## Past Courses

- Graphs Algorithms
- Data analysis
- Image Processing
- Graphical Models
- Kernel machines
- R Programming
- Matrix Calculus - Another Scilab reference card (in french)