Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissage

Collection Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissage

Organizer(s) Anthoine, Sandrine ; Chaux, Caroline ; Mélot, Clothilde ; Richard, Frédéric
Date(s) 25/01/2021 - 29/01/2021
linked URL https://conferences.cirm-math.fr/2472.html
00:00:00 / 00:00:00
5 16

Basics in machine learning - lecture 1

By Marianne Clausel

This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques. Class (4h) General Introduction to Machine Learning (learning settings, curse of dimensionality, overfitting/underfitting, etc.) Overview of Supervised Learning: True risk/Empirical risk, loss functions, regularization, sparsity, norms, bias/variance trade-off, PAC generalization bounds, model selection. Classical machine learning models: Support Vector Machines, Kernel Methods, Decision trees and Random Forests. An introduction to uncertainty in ML: Gaussian Processes, Quantile Regression with RF Labs (4h) Introduction to scikit-learn Classical Machine learning Models with scikit-learn Uncertainty in ML

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Citation data

  • DOI 10.24350/CIRM.V.19704403
  • Cite this video Clausel, Marianne (25/01/2021). Basics in machine learning - lecture 1. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19704403
  • URL https://dx.doi.org/10.24350/CIRM.V.19704403

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