Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores cluster analysis methods and applications in genomic data analysis, covering classification, gene expression clustering, visualization, distance metrics, and clustering algorithms.
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Covers clustering time series data using dynamic time warping, string metrics, and Markov models.
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
