N/A
Basic
Intermediate
Expert
Reinforcement Learning (e.g., decision and control, planning, hierarchical RL)
Representation learning and Theory (e.g., control theory, learning theory, algorithmic game theory)
Optimization (e.g., convex and non-convex optimization)
Applications (e.g., speech processing, computational biology, computer vision, NLP)
Deep Learning (e.g., architectures, generative models, optimization for deep networks)
General Machine Learning (e.g., classification, unsupervised learning, transfer learning)
Probabilistic Methods (e.g., variational inference, causal inference, Gaussian processes)
Social Aspects of Machine Learning (e.g., AI safety, fairness, privacy, interpretability)
Infrastructure (e.g., datasets, competitions, implementations, libraries)
Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)