Space period: 08.08.22 - 27.08.22
Human-Centered Machine Learning is an innovative and hands-on bachelor's level course on the main components and combinations of current machine learning systems. This two-week course will teach you some of the most widely used machine learning (ML) techniques. The focus will be on human-centered applications of ML methods that require high levels of privacy protection and transparency. The course includes lectures that teach basic principles of human-centered ML and its applications (such as elderly care). You will learn to implement privacy-preserving and transparent ML methods by using a few lines of Python during exercise sessions.
The amount of data in the world is increasing exponentially. For the future, we need machine learning models and other algorithms to make use of all the data that is constantly generated. AI and Machine learning are the most powerful tools that organisations are using today to make informed decisions, attract new customers and find new sources of revenue.
During the course, you will learn how to decompose machine learning into three basic components: data, model and loss. The lecture content consists of practical examples of machine learning applications that involve different types of data points such as persons, proteins or days, and how the components can be combined using the principle of empirical risk minimization (ERM). A plethora of machine learning methods is obtained for different design choices in ERM.
You will learn to apply these machine learning methods using a few lines of Python code and to diagnose them using powerful validation techniques.
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