Clustering: k-meansExplains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Graph Coloring IIExplores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Time Series ClusteringCovers clustering time series data using dynamic time warping, string metrics, and Markov models.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Efficient Data ClusteringCovers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.