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Enseignement ATHENS - TPT40 : Practice of Large Scale Machine Learning (Télécom Paris - Palaiseau)

Domaine > Informatique.

Descriptif

This course will provide the basics in supervised problems both from a machine learning (logistic regression, random forest, xgboost, etc.) and deep learning (MLP, convolutional and recurrent networks, etc.) perspective.
The course will attempt at providing a balanced theoretical-practical approach top the above problems and methods.
Prerequisites are good knowledge in probability for the machine learning part and good knowledge of Python (and possibly of Jupyter notebooks) especially for the deep learning part.

Objectifs pédagogiques

Day 1: Introduction to Pandas and Scikit-learn – Logistic regression – The Titanic dataset.

Day 2: Feature engineering - Random Forest, xgboost – The Avazu dataset.

Day 3: Continuation of work on Avazu dataset - Introduction to neural networks

Day 4: Exercises in MLPs - Convolutional networks and deep architectures

Day 5: Exercises in convolutional networks

effectifs minimal / maximal

1/30

Diplôme(s) concerné(s)

Format des notes

Numérique sur 20

Littérale/grade européen

Pour les étudiants du diplôme Echange non diplomant

Pour les étudiants du diplôme Diplôme d'ingénieur

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 3 ECTS
  • Crédit d'UE électives acquis : 3

La note obtenue rentre dans le calcul de votre GPA.

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