Machine Learning Fundamentals

This lecture covers the basics of machine learning, including supervised and unsupervised learning, linear regression, classification, and regression algorithms. It delves into topics such as PCA, LDA, kernel methods, deep generative models, and dimensionality reduction techniques like t-SNE and VAE. The instructor explains the concepts using examples from document analysis, word embeddings, and sequence-to-sequence models.