+++ Due to the Covid-19 pandemic the TU Berlin Summer University will not take place on campus this year. Registrations  for our Summer University Online program  are now open! +++
In this course you will learn the basic knowledge of machine learning and some applications for machine learning with the use of the programming language Python. You will be working at the computer. Through many different tasks you get to know several topics in this area and you will acquire some fundamental skills in Python.
Learning Goals and Syllabus
Upon the completion of the course you will be able to understand and write scripts in Python and use specific software libraries for your case of machine learning task.
Please click here  to see the syllabus.
The course will include the following topics:
- Python syntax
- Logistic regression
- Neural nets
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Document classification
- Image classification
- Game ai development
The general prerequisites of the TU Berlin Summer University are the following: at least one year of university experience + English level B2 or equivalent.
In computer science it is recommended to know:
- What a variable, assignment and statement means
- What a software library and an interface means
- How a loop and a conditional statement generally works
Some basic knowledge of console commands, especially those related to file management is a plus.
Lars Gehrke worked as a software developer for the IT service provider of the German Chambers of Commerce and Industry for five years.
He finished his apprenticeship 2016 and his B.Sc. 2018 at the University of Applied Sciences and Arts in Dortmund.
He wrote his bachelor thesis in the field of machine learning with python on behalf of his company and his own IT startup in 2018.
Please direct questions about the course to the TU Berlin Summer University Team at: summeruniversity(at)tubs.de . We will answer your questions and direct specific queries regarding course content to the course lecturers where necessary.