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The regular machine learning (ML) track was a context-aware travel mode recommendation problem, sponsored by Baidu.com. The automatic machine learning (AutoML) track, sponsored by 4Paradigm, was about nding cost-e ective and transferable solutions for temporal relational data represented as multiple related tables. The humanity reinforcement learning (RL) track was sponsored by IBM Africa and Hexagon-ML to determine the best policy in distribution of control measures to eradicate Malaria. In 3 competitions collectively, we had more than 2,800 registered teams from over 39 countries and 230 academic and research institutions. Among the 1,200 most actively participating teams, over 5,000 individuals participated, and more than 17,000 submissions were made. 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