Here is a (not comprehensive) list of topics I didn’t go through during my undergrad, but it all seems super intriguing for me.

CS726 Advanced ML IITB

• probabilistics graphical models (directed and undirected),
• inference methods like junction trees,
• belief propagation,
• MCMC sampling methods like Gibbs and Langevin,
• generative models like variational auto-encoders, GANs,
• Deep Gaussian processes,
• neural architectures for structured predictions,
• neural density estimation methods,
• causality

ML @IITK

• Latent variable models: expectation-maximization for learning latent variable models
• Ranking methods (also related to search, my work domain at Carousell)
• Sequence Tagging
• Manifold Learning
• Sparse modelling and estimation
• Online learning algorithms: perceptron, Widrow-Hoff, explore-exploit
• Statistical learning theory: PAC learning, VC dimension, generalization bounds