Covers the basics of Natural Language Processing, from traditional to modern approaches, highlighting the challenges and importance of studying both methods.
Explores clustering methods for partitioning data into meaningful classes when labeling is unknown, covering K-means, dissimilarity measures, and hierarchical clustering.
Covers the basics of tensors, including their definition, properties, and decomposition, starting with a motivating example involving Gaussian distributions.
