Vous consultez actuellement ce cours en tant que Guest.
Résumé de section
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27.4. Session #1: Introduction- Recap of DS4DH1
- Course organization
4.5. Session #2: Recap of DSDH1
- Pandas
- Numpy, Scipy
- Seaborn
11.5. Session #3: Corpus linguistics
- Lexical association measures
- Multi-word expressions, collocations, idioms
- Lexico-semantic resources: WordNet, BabelNet, PanLex
22.5. Session #3: Topic modeling -- exceptionally on Monday, 14-16
- Latent Dirichlet Allocation
- Practical examples with LDA in Gensim
- Homework project #1: pick a corpus, induce topics, analyze topics and topical distribution of documents, prepare a small-scale presentation
25.5. Session #4: Networks
- Introduction to Graph Theory
- Node importance -- degree centrality, closeness centrality, betweeness centrality
- Shortest paths
- Practical exercises with networkx
- Homework project #2: analysis of a large-scale network dataset; prepare a small-scale presentation with insights
1.6. Session #5: Evaluation & Statistical Testing
- Common evaluation measures for classification and regression
- Gold-standard annotation and inter-annotator agreement
- Significance testing (parametric: Student’s t-test; non-parametric: Wilcoxon’s test
15.6. Session #6: Student presentations -- Topic Modeling Homeworks
22.6. Session #7: Student presentations -- Network Analysis Homeworks
29.6. Session #8: Deep Learning
- Convolutional NNs
- Recurrent NNs
- Attention mechanism and Transformers
- Practical exercises in keras
6.7. Session #9: Interpretability & Fairness- Explainability and interpretability of machine learning models
- Biases and fairness: data bias, model bias
13.7. Session #10: Guest Lecture
- A talk by a prominent researcher in the area of Computational Humanities
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