Explores autocorrelation, periodicity, and spurious correlations in time series data, emphasizing the importance of understanding underlying processes and cautioning against misinterpretation.
Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
