Abschnittsübersicht

◄Part 5: Explorative Analysis 1 – Descriptive Analysis and visualization
►Part 7: Predictive Analysis (A Gentle Introduction to Machine Learning)

    • Lecture content: Basics of vector spaces (in a practical, not strictly mathematical sense; e.g., vectors are points in an n-dim space...) and distance functions. 

      Clustering algorithms (KMeans, hierarchical clustering, (potentially also DBSCAN))

      Tutorial content: Fundamentals and philosophy of the scikit-learn API. Transforming data and using the clustering algorithms.

      Homework: Usage scenario – Clustering medieval scripts using computer image analysis (and different clustering algorithms).


    • Notebook: Clustering and Distance Functions (HTML) Datei
    • Notebook: Clustering and distance function (.ipynb) Datei
◄Part 5: Explorative Analysis 1 – Descriptive Analysis and visualization
►Part 7: Predictive Analysis (A Gentle Introduction to Machine Learning)