Networked Artificial Intelligence

About this programme

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information. Please note: all information from the Course syllabus is available on this page in an accessible format. The course covers both machine learning and classical AI paradigms in a network context. Here, the concept of network is understood in a broad scientific sense (e.g. networks of agents, sensors, computing nodes, social networks or communication networks). The course covers theories, methods and practical applications of artificial intelligence in network environments. The focus is on both machine learning and other AI paradigms such as search, planning, reasoning and multi-agent systems. The course is divided into three parts: The course includes both theoretical lectures and exercises. The exercises consist of mathematical problems, analysis of algorithm properties, and practical implementations in Python or a similar programming environment. in order to prepare the student for research or qualified engineering work in artificial intelligence for distributed and networked systems. Knowledge in linear algebra, 7.5 credits, equivalent to a completed course such as SF1624/SF1672/SF1684. Knowledge in multivariable analysis, 7.5 credits, equivalent to a completed course SF1626/SF1674/SF1686. Knowledge in probability theory and statistics, 6 credits, equivalent to a completed course SF1910-SF1925/SF1935 or completed exam module TEN1 within SF1910/SF1925/SF1935.