Optimization in Machine Learning

This lecture covers optimization techniques in machine learning, focusing on loss functions, probability models, and computing derivatives for backpropagation. It explains the concepts of hierarchical softmax, word embeddings, and GloVe model. The lecture also delves into subword embeddings, FastText, and Byte Pair Encoding. It discusses the challenges of overfitting and the importance of regularization in model complexity. Additionally, it explores collaborative and content-based recommendation systems, matrix factorization, and latent semantic indexing.