Basics of Machine Learning

RWTH Aachen University is one of Germany’s leading technical universities, founded in 1870 and located in the historic city of Aachen. Known for its strengths in engineering, natural sciences and technology, RWTH blends a long tradition of applied research with close industry partnerships and a clear focus on innovation. With over 47,000 students, including more than 13,000 international learners, the university offers a dynamic environment where practical problem solving meets academic rigor.

The university hosts 51 program areas supported by world-class laboratories and collaborative research centers on the RWTH Campus. Students benefit from project-based coursework, internships with local and global companies, and a culture that encourages entrepreneurship and interdisciplinary work. English-taught master’s degrees and comprehensive student services make RWTH accessible to international applicants seeking research-led study and pathways into Europe’s advanced engineering sectors.

Aachen is compact and student-friendly, offering affordable housing options, efficient transit to nearby German and Dutch cities, and a lively cultural scene that balances history and modern student life. RWTH’s international office, career centers and language courses provide practical guidance on visas, funding and settling in, while numerous student associations support networking and integration. Scholarship and tuition information is available online to help plan finances, making RWTH a strong choice for students who value hands-on engineering education and close industry engagement.

The Basics of Machine Learning program at RWTH Aachen University is a non-degree course for students who want to learn about machine learning fundamentals. It lasts one week and is taught in English. This program provides students with a solid foundation in machine learning concepts and techniques.

The curriculum covers subjects like supervised and unsupervised learning, neural networks, and deep learning. Students develop skills in data preprocessing, model evaluation, and hyperparameter tuning. Hands-on components include working with datasets and building machine learning models using popular libraries like scikit-learn.

After completing this program, students can pursue careers as Data Analysts, Business Intelligence Developers, or Machine Learning Engineers. They can work in industries like healthcare, finance, or marketing, and for employers like consulting firms or tech companies. Other potential job titles include Data Scientist or AI Researcher.