Section outline

• Выбрать раздел General

  General

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  • Выбрать элемент Language of the CourseThe lectures will be partial...

    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.

    
    
    

  • Выбрать элемент Announcements

    

    Announcements Форум
  • Выбрать элемент Questions and Discussions

    

    Questions and Discussions Форум
  • Выбрать элемент Zoom: Screenshare

    

    Zoom: Screenshare Собрание Zoom
• Выбрать раздел Content (planned)

  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

  
  
• Выбрать раздел Recap: Material from DS4HUM-1

  Recap: Material from DS4HUM-1

  • Выбрать элемент Notebook: Python-Basics (HTML-Version)

    

    Notebook: Python-Basics (HTML-Version) Файл
  • Выбрать элемент Notebook: Python-Basics 2 (HTML-Version)

    

    Notebook: Python-Basics 2 (HTML-Version) Файл
  • Выбрать элемент Notebook: Numpy (HTML-Version)

    

    Notebook: Numpy (HTML-Version) Файл
  • Выбрать элемент Notebook: Acquisition and Preprocessing (HTML-Version)

    

    Notebook: Acquisition and Preprocessing (HTML-Version) Файл
  • Выбрать элемент Notebook: Descriptive Statistics (HTML-Version)

    

    Notebook: Descriptive Statistics (HTML-Version) Файл
  • Выбрать элемент Notebook: Deep-dive into Seaborn (HTML-Version)

    

    Notebook: Deep-dive into Seaborn (HTML-Version) Файл
  • Выбрать элемент Notebook: Classification (.ipynb)

    

    Notebook: Classification (.ipynb) Файл
  • Выбрать элемент Slides: Text Classification and Clustering

    

    Slides: Text Classification and Clustering Файл
  • Выбрать элемент Notebook: Text processing (.ipynb)

    

    Notebook: Text processing (.ipynb) Файл
  • Выбрать элемент Slides: Lexical semantics

    

    Slides: Lexical semantics Файл
  • Выбрать элемент Slides: Text representations

    

    Slides: Text representations Файл
  • Выбрать элемент Slides: Information Extraction

    

    Slides: Information Extraction Файл
• Выбрать раздел Session 1: Introduction

  Session 1: Introduction

  • Выбрать элемент Jupyter Notebooks from DS4DH1 (HTML)

    

    Jupyter Notebooks from DS4DH1 (HTML) Файл
  • Выбрать элемент L1: Course introduction

    

    L1: Course introduction Файл
• Выбрать раздел Session 1.5: Recap

  Session 1.5: Recap

  • Выбрать элемент Notebook: Numpy & Pandas (HTML-Version)

    

    Notebook: Numpy & Pandas (HTML-Version) Файл
  • Выбрать элемент Notebook: Seaborn (HTML-Version)

    

    Notebook: Seaborn (HTML-Version) Файл
• Выбрать раздел Session 2: Corpus linguistics & lexicon-semantic resources

  Session 2: Corpus linguistics & lexicon-semantic resources

  • Выбрать элемент Notebook: Corpus Linguistics (HTML-Version)

    

    Notebook: Corpus Linguistics (HTML-Version) Файл
• Выбрать раздел Session 3: Topic Modeling

  Session 3: Topic Modeling

  • Выбрать элемент TM: Slides

    

    TM: Slides Файл
  • Выбрать элемент TM: Notebook (ipynb)

    

    TM: Notebook (ipynb) Файл
  • Выбрать элемент TM: Notebook (HTML)

    

    TM: Notebook (HTML) Файл
• Выбрать раздел Session 4: Networks

  Session 4: Networks

  • Выбрать элемент Notebook: Graphs & Network analysis (HTML-Version)

    

    Notebook: Graphs & Network analysis (HTML-Version) Файл
• Выбрать раздел Session 5: Evaluation & Statistical Testing

  Session 5: Evaluation & Statistical Testing

  • Выбрать элемент Note: Evaluation & Significance Testing (HTML-Version)

    

    Note: Evaluation & Significance Testing (HTML-Version) Файл
• Выбрать раздел Session 6: Student presentations of topic modeling

  Session 6: Student presentations of topic modeling
• Выбрать раздел Session 7: Student presentations on graph analysis

  Session 7: Student presentations on graph analysis
• Выбрать раздел Session 8: Deep Learning

  Session 8: Deep Learning

  • Выбрать элемент Intro2ML (from DS4DH1)

    

    Intro2ML (from DS4DH1) Файл
  • Выбрать элемент Intro2DL: Autoencoders, CNNs, RNNs

    

    Intro2DL: Autoencoders, CNNs, RNNs Файл
  • Выбрать элемент Notebook: Deep Learning (HTML)

    

    Notebook: Deep Learning (HTML) Файл
  • Выбрать элемент Notebook: Deep Learning (ipynb)

    

    Notebook: Deep Learning (ipynb) Файл
• Выбрать раздел Session 9: Interpretability & Fairness

  Session 9: Interpretability & Fairness

  • Выбрать элемент Notebook: Explainability & Fairness (HTML)

    

    Notebook: Explainability & Fairness (HTML) Файл
  • Выбрать элемент Notebook: Interpretability & Fairness (ipynb)

    

    Notebook: Interpretability & Fairness (ipynb) Файл
• Выбрать раздел Final Project

  Final Project

  • Выбрать элемент Submission deadline: 31.9.23Please use the provide...

    Submission deadline: 31.9.23
    

    Please use the provided template for styling your project report.
    

    Project reports can be either written in German or English.

    
    

  • Выбрать элемент Project data

    

    Project data Гиперссылка
  • Выбрать элемент Final Project: Description

    

    Final Project: Description Файл
  • Выбрать элемент Project report: LaTex Template

    

    Project report: LaTex Template Файл
  • Выбрать элемент Project report: Word template

    

    Project report: Word template Файл
  • Выбрать элемент Project report: Overleaf LaTex template

    

    Project report: Overleaf LaTex template Гиперссылка