Introduction to Machine Learning 2023
Modern methods from artificial intelligence and machine learning, in particular deep learning methods based on multi-layered artificial neural networks, provide unprecedented tools for data analysis and prediction. Over the past years, they have transformed many scientific fields and are have become ubiquitous in real-world applications from speech recognition to self-driving cars.
The course will provide a broad introduction to machine learning from statistical foundations to applications in the sciences, economics and engineering. The focus will be on modern machine learning methods for predictive data analytics such as random forests, gradient boosting machines and neural networks, their trans-disciplinary application to supervised learning tasks, and approaches to gain insight into the 'black box' of machine learning models. Lectures on the theoretical background will be accompanied by hands-on programming exercises in Python that will cover practical aspects of implementing machine learning methods for analyzing scientific and real-world datasets.
Practicalities: The course will take place in person from April 12 - 14, 2023. Some familiarity with basic concepts of probability theory and statistics is expected, as well as basic programming skills in Python. For the programming exercises, participants are expected to bring their own laptop with Python and relevant libraries installed. Technical instructions will be provided.
Application procedure: The number of participants is limited to 20. The application for participation in this course is open until March 20, at https://portal.wiwi.kit.edu/ys/6911. After the deadline, if you are selected as a participant, you will receive an offer to participate via the Wiwi-Portal platform which you will need to confirm within a few days, otherwise the spot will be given to someone else.
In case of any questions, you can contact me at Sebastian.Lerch∂kit.edu