Aperçu des sections

  • General

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    • Language of the Course

      The lectures will be partially in English and German.

      The material will be in English.

      Questions (in person and the Forum) can be asked in both languages.

      Proof of performance

      During the Course: Active participation, weekly assignments and project presentations.

      Final examination:

      To be announced.


      Schedule and location

      The sessions will take place Thursdays 10-12 c.t. in the Sensalight Technologies building (John-Skilton-Str. 8a), in room 4.23 (seminar room on the 4th floor). 

      Additional information

      Since each session contains hands-on parts, students are required to bring their laptops. 

      A working Python Stack (easiest to set up using Anaconda) has to be installed on your machines.

      If Anaconda is not yet installed on your machine, please install it before our first meeting so that we can solve eventual problems quickly together in the first lecture.

      Please also make sure to connect your machine to the university's official Wi-Fi (eduroam) since Bayern-WLAN is remarkably unreliable at times.



    • Announcements Forum
    • Questions and Discussions Forum
    • Zoom: Screenshare Réunion Zoom

  • Content (planned)


    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

  • Recap: Material from DS4HUM-1

  • Session 1: Introduction

  • Session 1.5: Recap

  • Session 2: Corpus linguistics & lexicon-semantic resources

  • Session 3: Topic Modeling

  • Session 4: Networks

  • Session 5: Evaluation & Statistical Testing

  • Session 6: Student presentations of topic modeling

  • Session 7: Student presentations on graph analysis

  • Session 8: Deep Learning

  • Session 9: Interpretability & Fairness

  • Final Project