Abschnittsübersicht

◄Part 6: Explorative Analysis 2 – Clustering and distance functions
►Part 8: Text and Language I (Computational Linguistics)

    • Lecture content: Basics concepts of (predictive) learning theory: inductive bias, generalization, overfitting; Traditional machine learning models: Logistic Regression, Naïve Bayes, k -Nearest Neighbours, Decision Trees, Support Vector Machines. Data splits and model selection. Evaluation metrics (accuracy, precision, recall) -- strengths and weaknesses. 

      Tutorial content: Training and evaluating concrete machine learning models on concrete datasets; Performing hyperparameter tuning and feature selection via cross-validation (Python libraries: scikit-learn).

      Homework: Usage scenario – Music genre classification. And to read a short paper on the implications of employing machine learning models in the humanities: Underwood, T. (2018). Algorithmic Modeling. Abingdon, Oxon ; New York, NY : Routledge,.

    • Notebook: Classification (.ipynb) Datei
    • Data: reviews_train.csv Datei
    • Data: reviews_text.csv Datei
    • Text Classification and Clustering: Slides Datei
◄Part 6: Explorative Analysis 2 – Clustering and distance functions
►Part 8: Text and Language I (Computational Linguistics)