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¡á General machine learning
(active learning, reinforcement learning, semi-supervised learning, etc)
¡á Deep learning
(tentative models, computer vision, acoustic analysis, natural language process, optimization)
¡á Probabilistic inference
(Bayesian methods, graphical models, Gaussian Process)
¡á FAT
(fairness, accountability, and transparency of AI)
¡á Application
(biology, climate science, healthcare, etc.)
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