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Basics in machine learning - practical session 2
Multi angle
Authors : Clausel, Marianne (Author of the conference)
CIRM (Publisher )
68-06 - Proceedings, conferences, collections, etc.
68T05 - Learning and adaptive systems
93B47 - Iterative learning control
CIRM (Publisher )
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Abstract :
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
MSC Codes : 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
68-06 - Proceedings, conferences, collections, etc.
68T05 - Learning and adaptive systems
93B47 - Iterative learning control
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 22/02/2021 Conference Date : 28/01/2021 Subseries : Research School arXiv category : Machine Learning Mathematical Area(s) : Computer Science Format : MP4 (.mp4) - HD Video Time : 01:25:24 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2021-01-26_Clausel_4.mp4 
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Information on the Event
Event Title : Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissageEvent Organizers : Anthoine, Sandrine ; Chaux, Caroline ; Mélot, Clothilde ; Richard, Frédéric Dates : 25/01/2021 - 29/01/2021 Event Year : 2021 Event URL : https://conferences.cirm-math.fr/2472.html
Citation Data
DOI : 10.24350/CIRM.V.19704703Cite this video as: Clausel, Marianne (2021). Basics in machine learning - practical session 2. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19704703 URI : http://dx.doi.org/10.24350/CIRM.V.19704703 |
See Also
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- [Virtualconference] Reinforcement learning - lecture 1 / Author of the conference Lazaric, Allesandro.
- [Multi angle] Basics in machine learning - practical session 1 / Author of the conference Clausel, Marianne.
- [Multi angle] Basics in machine learning - lecture 2 / Author of the conference Clausel, Marianne.
- [Multi angle] Basics in machine learning - lecture 1 / Author of the conference Clausel, Marianne.
Bibliography
