Deep learning at ENS Ulm

• Automatic differentiation
• Multilayer Perceptrons: Expressiveness, overfitting, regularization
• Embeddings: learning representations for unsupervised learning
• Sequences: seq2seq, attention

Competitive programming at École polytechnique

See all slides.

• Intro to ICPC
• Intro to pathfinding
• Strings
• Matching & flows
• Segment trees

Machine learning

Jupyter notebooks on GitHub

Supervised learning: regression, classification

• Regression, classification
• Write your own gradient descent
• Write your own backpropagation
• Classification of handwritten digits

Unsupervised learning

• Dimensionality reduction
• Recommender systems
• k-means
• Skip-gram & negative sampling: word2vec
• t-SNE

Reinforcement learning

• Policy improvement
• Value iteration

Préparation à l’agrégation d’informatique

• Exemples d’algorithmes d’apprentissage supervisé et non supervisé : KNN, K-moyennes
• A* : heuristiques pour les plus courts chemins
• Hors programme : Modèles graphiques probabilistes
• TP Viterbi : Doudou le hamster surveillé