Introduction to Data Science and Artificial Intelligence
Full course description
The course Introduction to Data Science and Artificial Intelligence offers a comprehensive overview of the core topics in Data Science and Artificial Intelligence, both from a mathematical and from a computational perspective. Particular emphasis is put on the basic classes of techniques and methods, the theoretical underpinnings of data science and computational intelligence, and some example application domains of data science and artificial intelligence. As such, the course provides an overview of many topics that are addressed in much more detail throughout the Bachelor’s Data Science and Artificial Intelligence programme. Knowledge and understanding: After successful completion of the course, students will be able to recognise what real world problems require the use of data science, and approach their solution by using a data science process, namely: explore the data, model the data, and perform simulations if required. Moreover, they will exhibit knowledge in the basic concepts of artificial intelligence, such as agents, search, artificial intelligence, decision trees. Applying knowledge and understanding: Students learn to recognise applications of data science and artificial intelligence in different domains and apply the basic techniques they have learnt from both.
Prerequisites
None.
Desired Prior Knowledge: The course appears as desired prior knowledge for the courses Reasoning Techniques and Theoretical Computer Science
Recommended reading
- S. Russell and P. Norvig (2010): Artificial Intelligence, A Modern Approach. Third edition, Pearson Education, ISBN 978-0-13-207148-2.
- C.D. Manning, P. Raghavan and H. Schütze (2008) Introduction to Information Retrieval. Cambridge University Press. ISBN 0521865719