描述
不确定性、不确定性传播、贝叶斯推断、传感器融合、时间序列、高斯过程、将科学/用户知识整合进机器学习、用于微分方程的神经网络、概率性深度学习、序列决策。案例研究来自生物医学、自动驾驶、传感器与信号处理等多个领域。
原文参考文本
Uncertainty, uncertainty propagation, Bayesian inference, sensor fusion, time series, Gaussian processes, integrating scientific/user knowledge into machine learning, neural networks for differential equations, probabilistic deep learning, sequential decision making. Case studies will be drawn from various fields including biomedical, autonomous vehicles, sensors, and signal processing.
来源与参考
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来源参考 : https://calendar.carleton.ca/grad/courses/COMP/