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.
Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.
Explores image compression through various approaches like pixel and block level compression, Discrete Cosine Transform, quantization, and entropy coding.
