|151-0118-00L||Applied Machine Learning for Engineers |
Number of participants limited to 40.
|4 credits||3G||B. Vennemann|
|Abstract||Introduction to the most frequently used methods of machine learning, including regression, classification, dimensionality reduction and selected topics of deep learning, including artificial neural networks, convolutional neural networks, recurrent neural networks and autoencoders. This lecture has a strong practical focus with programming sessions.|
|Objective||An understanding of the various tools within the machine learning landscape. Ability to select an appropriate method and to build, train and evaluate a model using Scikit-learn and Keras.|
|Content||Data preprocessing, regression, classification, dimensionality reduction, artificial neural networks, convolutional neural networks, recurrent neural networks, autoencoders.|
|Lecture notes||Lecture notes will be distributed electronically.|
|Prerequisites / Notice||Basic knowledge of the Python programming language. This course is mainly targeted towards master-level students of mechanical or process engineering.|