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$K$-means Clustering Algorithm via Vector Quantization

Published:

The talk is about a mathematical model of data compression known as Vector Quantization and its algorithmic adaptation, the well-known K-means algorithm. The goal of this talk is to show how the mathematical theories of vector quantization model became the fundamental structure of K-means clustering algorithm. The slide can be found here.

Fundamental and Hands-on Introduction to Machine Learning

Published:

This talk aims at providing key concepts of Machine Learning through real examples. Linear models in regression and classification are introduced along with some tasks. Some gradient-based optimization and penalization techniques are also discussed from Linear Regression to Deep Neural Networks. The slide can be found here: Introduction to Machine Learning. For those who want to play with the codes, you can find jupyter notebook of the simulation here: Teaching repository.

teaching

UFR de Mathématiques - Université Paris Cité

License and Master's degree courses, Université Paris Cité, UFR de Mathématiques, 2018

Master’s degree (2018 - 2023)

  • M2MO : TD of Statistical Modeling.

  • M1ISIFAR : TP R-programming of Data Analysis.

  • M2ISIFAR : TP R-programming of Data Mining.

  • M1MIDS : TP R-programming of Exploratory Data Analysis.

  • M1 Math-Info : TP of Big data technology with Spark and Python.

License (2022)

  • L3 Info : TP of Algorithm and programming in Python.