Machine Learning for Complex Networks

Algorithms, AI, and Data Science II

Artificial intelligence (AI) and data science have become key technologies that are transforming virtually all aspects of science, industry, and society. They enable us implement systems that are able to perceive their environment, formally represent knowledge about the world, and engage in automatic reasoning and problem-solcing, possibly based on incomplete or uncertain information. But how can we use machines to formally represent knowledge about the world and how can we use algorithms to automatically derive conclusions based on prior knowledge and empirical observations? How can we measure the information content of data and how can we detect relevant patterns and causal relationships? And which algorithmic strategies can we use to address common optimization problems in artificial intelligence and machine learning?

Building on the first part Algorithms, AI, and Data Science I, this subsequent course will equip BSc students with key concepts and algorithms that are the foundation of computer science, artificial intelligence, and data science. The course will introduce formal computational models that are used to study and define concepts like "computability'' or "machine intelligence''. It further shows how formal logics can be used for knowledge representation and automatic reasoning.

Students will understand how statistical methods can be used to reason about data, how we can use mathematical approaches to formalize concepts like information and how algorithms can be used to compress data and find significant patterns. Addressing data with complex topologies, a final part of the lecture covers advanced algorithms for graph-theoretic and geometric problems.

The course combines a series of lectures -- which introduce basic concepts and algorithms in computer science, AI, and data science -- with weekly exercises that show how we can implement and apply those algorithms in python. The course material consists of lecture slides and jupyter notebooks. Students can apply and deepen their knowledge through weekly exercise sheets. The successful completion of the course requires to pass a final written exam.