Computer-implemented method for training a machine learning routine (BNN) for controlling a technical system (TS), wherein the technical system (TS) is a medical diagnostic apparatus having at least one sensor S and an evaluation device AW, trained by way of a machine learning method, for evaluating sensor data of the sensor S, wherein the at least one sensor S is in the form of a microscope that records microscopic images PIC of biological tissue or of biological cells and transmits them to the evaluation device (AW), wherein a type or a state of the tissue or of the cells and/or a respective pathological class is recognized in the evaluation, wherein a) a sequence of first training data (PIC) comprising first sensor data of the medical diagnostic apparatus is read in by the machine learning routine (BNN) of the evaluation device, b) the machine learning routine (BNN) is trained on the basis of the first training data (PIC), wherein a plurality of learning parameters (LP) of the machine learning routine (BNN) is set by the training, c) a value distribution (VLP) of learning parameters (LP) that occurs during the training is determined, d) a continuation notification (CN) is generated as a function of the determined value distribution (VLP) of the learning parameters (LP), and e) depending on the continuation notification (CN), the training is continued with a further sequence of the first training data (PIC), or other training data (PIC2) are requested for the training, wherein, depending on the continuation notification (CN), the at least one sensor is controlled in such a way that a sensor parameter of the sensor for acquiring the other training data (PIC2) is modified, or a different sensor is used to acquire the other training data (PIC2), wherein the other training data (PIC2) are recorded by the microscope, by a different microscope and/or a different camera, wherein the other training data (PIC2) are recorded with a higher image resolution, different zoom level and/or in a different spectral region than the first training data (PIC). a) a sequence of first training data (PIC) comprising first sensor data of the medical diagnostic apparatus is read in by the machine learning routine (BNN) of the evaluation device, b) the machine learning routine (BNN) is trained on the basis of the first training data (PIC), wherein a plurality of learning parameters (LP) of the machine learning routine (BNN) is set by the training, c) a value distribution (VLP) of learning parameters (LP) that occurs during the training is determined, d) a continuation notification (CN) is generated as a function of the determined value distribution (VLP) of the learning parameters (LP), and e) depending on the continuation notification (CN), the training is continued with a further sequence of the first training data (PIC), or other training data (PIC2) are requested for the training, wherein, depending on the continuation notification (CN), the at least one sensor is controlled in such a way that a sensor parameter of the sensor for acquiring the other training data (PIC2) is modified, or a different sensor is used to acquire the other training data (PIC2), wherein the other training data (PIC2) are recorded by the microscope, by a different microscope and/or a different camera, wherein the other training data (PIC2) are recorded with a higher image resolution, different zoom level and/or in a different spectral region than the first training data (PIC). <EOS>