Conventional machine learning (ML) relies heavily on manual design from experts to decide tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive, time-consuming, cannot learn autonomously like humans. In education science, self-directed learning, where human learners select tasks materials their own without requiring hands-on guidance, has been shown be m...

Abstract A significant set of epistemic and political transformations are taking place as states societies begin to understand themselves their problems through the paradigm deep neural network algorithms. machine learning order does not merely change technologies governance, but is itself a reordering politics, what can be. When algorithmic systems reduce pluridimensionality politics output mo...

Machine learning techniques have been proposed to optimize the databases. For example, traditional empirical database optimization (e.g., cost estimation, join order selection, knob tuning, index and view advisor) cannot meet high-performance requirement for large-scale instances, various applications diversified users, especially on cloud. Fortunately, machine based can alleviate this problem ...