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Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.
Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.
Explores the stochastic blockmodel, spectral clustering, and non-parametric understanding of blockmodels, emphasizing metrics for comparing graph models.
Introduces the Missing Maps Project, a collaboration to map vulnerable areas before disasters using remote and community volunteers, aiming to engage people between disasters and work in remote areas.
Explores optimization-based uncertainty quantification for ill-posed inverse problems in the physical sciences, focusing on regularization methods and interval constructions.