Generative Modelling

In this course, we will explore the theoretical and technical underpinnings of a range of generative models. We will start with a comprehensive study of fully-observed models, including Autoregressive models, Random Fields, Markov Models, and more. We will delve into the latent variable models such as probabilistic PCA, variational autoencoders and relevant re-parameterization techniques. Additionally, we will cover energy-based models like restricted Boltzmann machines, deep belief networks, as well as score-based models like diffusion models. Relevant inference methods such as maximum likelihood, Monte Carlo methods, exact methods, Laplace approximation, maximum a posteriori estimation, variational inference, and Markov Chain Monte Carlo, will be introduced. 

Furthermore, we will examine the practical applications that utilize generative modeling, particularly in the domains of Image Synthesis, Natural Language Processing (NLP), and multimodal learning.

Through this course, students will gain an understanding of generative models, contrast their strengths and weaknesses, and evaluate them for real-world tasks in various domains.