## 1 Introduction  
In recent years, artificial intelligence has become part of our life. Big companies such as Google, Apple, Amazon, and Netflix use machine learning for different tasks such as image classification, speech recognition, and making suggestions for products or movies. For a well-functioning algorithm, a huge amount of data is needed. In some cases, the available data are limited or costly and therefore the conventional machine learning approach may fail or perform badly. To ensure a well-functioning model with little data, hybrid machine learning can be applied. Hybrid means the combination of different methods, such as the combination of machine learning and physics--in form of a formula--or machine learning and methods from optimization theory. [PERSON] et al. [1] analysed the power consumption of a chiller whose physics are well-known. In their work, a machine learning model developed with an artificial neural network (ANN) and a hybrid machine learning model are compared. The hybrid model is a combination of an artificial neural network and regression equations based on physics. Both models perform well in predicting the power consumption. The main difference is that the hybrid model requires fewer inputs (four inputs) than the conventional one (eight inputs). In addition to that, it is shown that the hybrid model can be applied where the conventional ANN is unable to provide accurate predictions, for example, even when the parameters are outside of the valid input space of the ANN. A combination of a deep learning model and a non-dominated sorting generic algorithm, used to solve multi objective optimization, is developed by [PERSON] et al. [2] to find the optimal setpoints of proportional integral controllers. This model is trained with five scenarios identified by using fuzzy c-means algorithms and physical ratios. The hybrid approach enables a cost-effective and sustainable operation of a full-scale wastewater treatment under varying inflow conditions. [PERSON] et al. [3] combine two machine learning algorithms, K-means clustering and KNN classification, to reduce energy consumption by increasing the average utilization of physical machines in cloud data centres. The hybrid model is significantly better than the conventional ones. Many real-world machine learning tasks are faced with little data. [PERSON] and [PERSON] [4] compared different classifiers on small training sets. It is crucial to have meta-knowledge about which to apply in different situations. With little data, the question about the effect of the class distribution is important. [PERSON] and [PERSON] [5] analyse the relationship between the class distribution of the training set and the performance of classification trees induced from these data. In the machine industry, the monitoring of wear is a common research topic due to its potential for cost and resource savings. The fact that wear is in general not directly measurable requires the measure of a physical value that correlates with the state of wear to determine and interpret the condition of parts. [PERSON] and [PERSON] [6] measure the vibration of a tool longitudinally and transversely in the turning process with two accelerometers. The structure of the vibration measures is analysed using singular spectral analysis and clustering before presenting it to a neuronal network. Instead of using the vibrations, the acoustic emissions, recorded by a microphone, can be used. [PERSON] [7] shows methods based on acoustic emission sensing for tool wear monitoring in the turning process. [PERSON] et al. [8] use deep neuronal networks for fault characteristic mining and intelligent diagnosis of rotating machinery. Methods based on artificial neuronal networks with a shallow architecture may need manually extracted features, much prior knowledge, and diagnostic expertise. Using deeper architectures, deep neuronal networks, and massive data enables fault characteristic mining and diagnosis direct from raw data without additional knowledge.