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talks

$K$-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

University Paris Cité UFR de Mathématiques - Université Paris Cité

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

🎓 Master’s degree (2018 - 2023)

CourseYearProgram
TD of Statistical Modeling2018-2023M2MO
TP R-programming of Data Analysis2018-2023M1ISIFAR
TP R-programming of Data Mining2018-2023M2ISIFAR
TP R-programming of Exploratory Data Analysis2018-2023M1MIDS
TP of Big data technology with Spark and Python2022M1 Math-Info

🎓 Bachelor’s degree

CourseSeasonProgram
TP of Algorithm and programming in Python2022L3 Info