๐๏ธ Week 12 - Introduction to Generative Models
This week, we will start to explore generative models, which differ from discriminative models we have seen so far. We will begin by discussing the basic concepts of generative models, including the differences between generative and discriminative models, and the types of generative models. We will then cover a generative classifier (Naive Bayes) and how it can be used for classification tasks. Then, we will discuss the concept of latent variables and how they can be used to model complex data distributions. We will start to explore easy latent variable models, including Gaussian Mixture Models (GMMs). Finally, we will talk about Principal Component Analysis (PCA) and how it can be expressed as a latent variable model, including its probabilistic interpretation and how it can be used for dimensionality reduction and data visualization.