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 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 me.

teaching/23-capsules.txt · Last modified: 2023/11/28 11:00 by marchand

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion