representation learning (including deep variational autoencoders etc.)

active learning (how a neural network can choose suitable training samples on its own)

reinforcement learning and optimization methods

graph neural networks

generative neural networks (learning to sample from an observed statistical distribution)

transformers and other attention-based methods

advanced concepts from information science and statistics (e.g. mutual information)

automated program discovery

applications in quantum physics, statistical physics, dynamical systems