Advanced learning for text and graph data ALTEGRAD
LearningNatural Language Processing

Objectif du cours

The ALTEGRAD course ( 28 hours) aims at providing an overview of state-of-the-art ML and AI methods for text and graph data with a significant focus on applications. Each session will comprise two hours of lecture followed by two hours of programming sessions.

Grading for the course will be based on a final data challenge plus lab based evaluation.


All interested students should fill the following form to enrol: link to form

The ALTEGRAD challenge Fall 2017 topic is:
« Can you predict whether two short texts have the same meaning? »

You can find more information for the challenge here

Course web page: here

Course Syllabus 2017 :

  •  Graph-of-words advanced topics (1/2)
    -Introduction to text preprocessing and NLP
    -graph of words and information retrieval
  • Graph-of-words advanced topics (2/2)
    -Keyword extraction, Summarization (abstrative, extractive)
    -Graph based document categorization
    -Event detection in text streams (twitter)
  • Word & document embeddings
    -Introduction to NN architectires for NLP
    -Latent Semantic Indexing
    -Word embeddings
    : word2vec, glove models, word mover’s distance, doc2ve

Course Syllabus 2017 :

  • Deep learning for NLP-Backpropagation for MLP architectures
    -Deep Learning architectures for text classification (CNNs, RNNs, LSTMs)
  • Influential spreader detection, influence maximization
    -single spreaders: epidemic models (SIR/SIS), baselines for spreaders selection
    -multiple spreaders:  LT/IC model, Greedy Algorithm
  • Graph kernels, community detection
    -Graph Generators, Similarity, Kernels, Clustering/applications)
  • Deep Learning for Graphs (DeepWalk, Node Embeddings
    – DeepWalk, LINE, GraRep, node2vec, Graph Classification with  Patchy-San, Spectral Networks
Les intervenants

Michalis Vazirgiannis


voir les autres cours du 1er semestre