151-0118-00L  Applied Machine Learning for Engineers

SemesterSpring Semester 2020
LecturersB. Vennemann
Periodicitynon-recurring course
Language of instructionEnglish
CommentNumber of participants limited to 40.


151-0118-00 GApplied Machine Learning for Engineers3 hrs
Fri13:15-16:00ML H 44 »
B. Vennemann

Catalogue data

AbstractIntroduction 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.
ObjectiveAn 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.
ContentData preprocessing, regression, classification, dimensionality reduction, artificial neural networks, convolutional neural networks, recurrent neural networks, autoencoders.
Lecture notesLecture notes will be distributed electronically.
Prerequisites / NoticeBasic knowledge of the Python programming language. This course is mainly targeted towards master-level students of mechanical or process engineering.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersB. Vennemann
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 30 minutes
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


Places40 at the most
Waiting listuntil 08.02.2020

Offered in

Mechanical Engineering MasterEnergy, Flows and ProcessesWInformation
Process Engineering MasterCore CoursesWInformation