Artificial Intelligence and Machine Learning (AIML) for Connected Systems
The main goal of this course is to cover the basic concepts related to machine learning projects and present the main ML models and algorithms and how to apply them to connected systems.
The course intercalates theoretical lectures and lab sessions. The main idea consists of presenting the theoretical background of a specific subject, followed by a lab session in which students will learn more details about each model and algorithm with practical examples using the most popular tools and libraries available. The course includes hand-on lab sessions with practical assignments, some of which are evaluated.
The course is connected-systems oriented, which means that, in addition to the most popular datasets, like MNIST and California houses, students will also see other examples of network-related datasets.
Sprache: englisch
Topics:
(90 LP/ECTS) — Berufsbegleitendes Weiterbildungsstudium
The part-time microcredential is designed for professionals in technical, scientific, and engineering fields who wish to expand their knowledge of artificial intelligence (AI) and specialize in its application to connected industrial environments. It is particularly suited for professionals working in sectors such as mechanical engineering, robotics, information technology, electronics, automotive engineering, or automation technology who want to acquire hands-on expertise in AI and connected technologies.
The study program combines self-study and group work in a flexible online learning environment. Students have access to video lectures, a detailed and user-friendly script tailored for working professionals, as well as interactive quizzes and exercises. Regular tutorial sessions and online office hours with mentors support the learning process, while discussion forums facilitate exchange among students. For more detailed information, please refer to the module handbook.
After the lectures and lab activities, students will demonstrate their competence by taking part in a Kaggle competition in which they design and train models of their choice to solve a real-world communication networks use case.
A foundational understanding of Python is required, including basic syntax, data structures, and functions. Some prior familiarity with NumPy is a plus. Because all lab exercises in this course are well-guided, you are not expected to have prior experience with machine learning libraries.
For those with little or no prior experience, there is the opportunity to take an introductory Python course to gradually acquire the necessary foundational knowledge.
This course can be credited toward the elective section of the study program. Further information
“Introduction to Programming with Python for Data Science” is the fundamental course, and “Machine learning with Python” builds upon it. Both courses can be credited toward the free elective section of the program.
