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Humans have a great ability to perceive the world around in details. We can analyze objects and their properties, we can identify anomalies in medical images, we can single out people in images and describe their actions. Automatic machine-understandable methods for such applications are integral parts of most human-centric applications. On the other hand, humans require interpretability of such machine learning techniques, from understanding of visual scenes for self-driving applications to analyzing neuroimages to diagnose diseases and their underlying reasons (biomarkers). Despite several successes in these fields, such detailed and interpretable understanding of visual data is beyond current computer vision technology. In this talk, I discuss interpretable machine learning techniques from pre- and post-deep-learning eras for applications of analyzing humans from videos or brain neuroimages. Specifically, I will discuss ¡°Understanding Actions in Videos¡±, ¡°Visual Reasoning for Future Forecasting in Videos¡±, ¡°Learning Brain Disease Biomarkers from NeuroImages¡±, and ¡°Human Brain Development in Early Years of Life¡±.