As the focus in the Wireless Sensor Networks and Sensor Systems community is shifting from “How do we collect data?” to “What can we learn from the data and how do the models look like?” we want to bring researchers from this community and the Machine Learning community together. Working with sensor data, machine learning methods become more and more popular (e.g., at the ACM SenSys conference – the major conference in this area – in 2013 the First International Workshop on Sensing and Big Data Mining (SenseMine) took place). As the applications for machine learning expand into other areas, the need for high-quality machine learning methods constantly grows. Additionally, there is a need for interpretable models as researchers want to grasp the models and get a sense of how the sensor information is combined in the model. However, sensor data poses a number of unique challenges for machine learning. Ranging from missing values, unreliable measurements, missing calibration to high spatial diversity. Most challenges have not been addressed with a focus on real-world sensor data. It is our belief that a discussion will help foster new results in the intersection of both communities.