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Accueil > Formations > Master MVA > Présentation des cours
Advanced learning for text and graph data ALTEGRAD
Lecturer : Michalis Vazirgiannis (Polytechnique)
Introduction :
The ALTEGRAD course 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.
Video de présentation
Logistic 2021 :
7 sessions of 4 hours : Each session = two hours of lecture followed by two hours of programming sessions.
Evaluation : Grading for the course will be based on a final data challenge plus lab based evaluation :
- 20% lab assignments
- 80% data challenge performance (report/creativity/leaderboard score/)
Course web page / moodle : https://moodle.lix.polytechnique.fr/moodle/
Schedule 2021 : always 14:00 - 18:00 / Synchronous video classes (most likely zoom)
- 17, 24 Nov 2020
- 1,8, 15 Dec 2020
- 12, 19 Jan 2021
Mandatory registration :
In order to
- get access to the teaching / lab material
- Receive our announcements
all interested students must enroll and fill the following form at
Course Syllabus 2021-22 :
1.1 TEXT/NLP - Graph based Text Mining
- Graph-of-words GoWvis
- Keyword extraction (TFIDF, TextRank, ECIR'15, EMNLP'16)
- Extractive summarization (EMNLP'17)
- Sub-event detection in twitter streams (ICWSM'17)
- graph based document classification: TW-IDF (ASONAM'15), TW-ICW, subgraphs (ACL'15)
- abstractive summarization - ACL 2018 summarization
1.2 TEXT - NLP - Word & doc embeddings (P) - Word embeddings: word2vec-glove models, doc2vec, subword, Latent Semantic Indexing, context based embeddings
- doc similarity metrics: Word Mover's distance, shortest path kernels (EMNLP16)
1.3 Deep learning for NLP
1.4 Graph kernels, community detection
Grakel python library: https://github.com/ysig/GraKeL/
1.5 Deep Learning for Graphs - node classification
1.6 Deep Learning for Graphs - Graph classification graph CNNs
- message passing
- Graph - Auto-encoders
1.7 Sets embeddings - point clouds
1.8 Network Architecture Search - interpretability.