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Nützliche Links Minimizza Espandi
Veranstaltungssuche Rechenzentrum Website-Support Häufige Fragen Lehre Digital Forschung Digital Lecture - Videoupload CaseTrain
Espandi tutto Minimizza tutto
Espandi Minimizza General Hervorgehoben
  • Language of the CourseThe lectures will be partial...
  • Announcements
  • Questions and Discussions
  • Zoom: Screenshare
Espandi Minimizza Content (planned) Hervorgehoben
Espandi Minimizza Recap: Material from DS4HUM-1 Hervorgehoben
  • Notebook: Python-Basics (HTML-Version)
  • Notebook: Python-Basics 2 (HTML-Version)
  • Notebook: Numpy (HTML-Version)
  • Notebook: Acquisition and Preprocessing (HTML-Version)
  • Notebook: Descriptive Statistics (HTML-Version)
  • Notebook: Deep-dive into Seaborn (HTML-Version)
  • Notebook: Classification (.ipynb)
  • Slides: Text Classification and Clustering
  • Notebook: Text processing (.ipynb)
  • Slides: Lexical semantics
  • Slides: Text representations
  • Slides: Information Extraction
Espandi Minimizza Session 1: Introduction Hervorgehoben
  • Jupyter Notebooks from DS4DH1 (HTML)
  • L1: Course introduction
Espandi Minimizza Session 1.5: Recap Hervorgehoben
  • Notebook: Numpy & Pandas (HTML-Version)
  • Notebook: Seaborn (HTML-Version)
Espandi Minimizza Session 2: Corpus linguistics & lexicon-semantic resources Hervorgehoben
  • Notebook: Corpus Linguistics (HTML-Version)
Espandi Minimizza Session 3: Topic Modeling Hervorgehoben
  • TM: Slides
  • TM: Notebook (ipynb)
  • TM: Notebook (HTML)
Espandi Minimizza Session 4: Networks Hervorgehoben
  • Notebook: Graphs & Network analysis (HTML-Version)
Espandi Minimizza Session 5: Evaluation & Statistical Testing Hervorgehoben
  • Note: Evaluation & Significance Testing (HTML-Version)
Espandi Minimizza Session 6: Student presentations of topic modeling Hervorgehoben
Espandi Minimizza Session 7: Student presentations on graph analysis Hervorgehoben
Espandi Minimizza Session 8: Deep Learning Hervorgehoben
  • Intro2ML (from DS4DH1)
  • Intro2DL: Autoencoders, CNNs, RNNs
  • Notebook: Deep Learning (HTML)
  • Notebook: Deep Learning (ipynb)
Espandi Minimizza Session 9: Interpretability & Fairness Hervorgehoben
  • Notebook: Explainability & Fairness (HTML)
  • Notebook: Interpretability & Fairness (ipynb)
Espandi Minimizza Final Project Hervorgehoben
  • Submission deadline: 31.9.23Please use the provide...
  • Project data
  • Final Project: Description
  • Project report: LaTex Template
  • Project report: Word template
  • Project report: Overleaf LaTex template
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  2. DSCM-Demo
  3. Example Session
  4. Example Lecture 1

Example Lecture 1

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Assignment 1 ►

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