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Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.
Introduces model-based clustering methods using mixture models and latent variables, with practical examples on iris data.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
