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Basics in machine learning - practical session 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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  • DOI 10.24350/CIRM.V.19704603
  • Cite this video Clausel Marianne (27/01/2021). Basics in machine learning - practical session 1. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19704603
  • URL https://dx.doi.org/10.24350/CIRM.V.19704603


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