==== Master, Bachelor or App Info project: Teaching Machine Learning ====
== Machine Learning Interactive Demos ==
**Supervision**: Stephane Marchand-Maillet

**Date of proposal**: Nov. 2023

In a continuing effort to create interactive pedagogic tools to teach Machine Learning, this project aims at creating an interactive (web) demo of a theorem/principle/algorithm in Machine Learning.
Topics include:
  * Optimization (eg Gradient descent, backprop, SGD)
  * Linear Algebra (eg eigen vectors, SVD)
  * Probabilities (eg sampling, change of variables)
  * Function analysis (eg Fourier, wavelets)
  * Concentration (eg distances, angles, WLLN, CLT)
  * Classical algorithms (eg ID3, Minimax, LogReg, Naive Bayes)
  * Visualization (eg NN, SVMs, clustering)
  * Manifold learning (eg tSNE, DiffMaps, UMAP, SOM)
  * Learning theory (eg Universal approximation, PAC learning, Hoeffding inequality)
  * ...

 The aim is to work out the topic "to the bone" to create a fundamental understanding and a high-level (intuitive) picture, then instantiated as an interactive demo (graphical or not).
See eg [[http://learn-ml.unige.ch/DT|decision trees]] (thanks to R. Maggio-Aprile and E. Icet, 2020 - A. Erne, 2022)

 There is therefore material to accommodate several students. Each project will be adapted to the level (App Info, Bachelor, Master,..)

If interested please contact [[stephane.marchand-maillet@unige.ch|me]].