Seminar Series Automotive Methods

Contact: Ferdinand Schockenhoff

In order to prepare students specifically for working on their theses at the FTM, different seminars on different topics are offered to students.

Each training topic is covered in a 1-2 hour session with presentation and exercises.

Registration is done individually for each topic by e-mail.


Getting started with MATLAB

For an overview of MATLAB, we recommend that students attend the official MATLAB training course (provided by the TU Munich):

https://www.mathworks.com/programs/services/training/display/tah-signup-tu-munchen.html

Offer:

  • MATLAB Fundamentals: Learn core MATLAB functionality including importing, analyzing, and exporting data.
  • MATLAB Programming Techniques: Learn the tools for writing, debugging, and profiling code.
  • MATLAB for Data Processing and Visualization: Discover how to import and prepare data for data analytics applications.
  • Machine Learning with MATLAB: This course shows how to use unsupervised learning techniques to discover features in large data sets and supervised learning techniques to build predictive models.

Contact: sam.matlab@ftm.mw.tum.de


DataCamp: Python, R und SQL

You want to learn Python, R or SQL? We offer more than 300 courses for beginners, advanced or expert users! The Institute of Automotive Technology offers free access to all DataCamp courses. In addition to the courses offered, you can refresh your programming skills in regular exercises and keep them active. Different "tracks" allow you to train as "Data Scientist/Engineer" or "Machine Learning Scientist" (for Python or R).

Offer (extract from the Python course offer): 

 

  • Introduction to Python; Intermediate Python; Importing, Cleaning and Analyzing Data
  • Introduction to SQL; Introduction to Relational Databases; Joining Data in SQL
  • Data Visualization with Python; Interactive Data Visualization with Bokeh; Clustering Methods with SciPy Supervised Learning with scikit-learn; Unsupervised Learning with scikit-learn; Introduction to Deep Learning in Python
  • Natural Language Processing; Image Processing (with Keras) in Python

You can register for free at DataCamp and complete the first chapter of any course free of charge. If you like the format and the mixture of theory and practice, we can activate you for further courses. Please contact us by giving your e-mail address (with which you are registered at DataCamp) Daniel Schröder.