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.
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
