Lecture
This lecture covers document retrieval and classification using TF-IDF matrices, cosine similarity, KNN method, and supervised methods. It also discusses sentiment analysis, regularization in linear regression, and topic detection through matrix factorization and latent semantic analysis. The instructor explains the challenges of sparsity in TF-IDF matrices and the transition from word vectors to contextualized word vectors like BERT, highlighting the importance of contextual information in natural language processing.