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)