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
Format des notes
Numérique sur 20Littérale/grade réduitPour 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.
Pour les étudiants du diplôme Echange non diplomant
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 3 ECTS
La note obtenue rentre dans le calcul de votre GPA.