the first lecture of Elements of Data Science and Artificial Intelligence will be held tomorrow (21st of October) at 12:15 by Prof. Jens Dittrich. Like all of Prof. Dittrich's lectures, it will be streamed to Youtube only, i.e. the lecture is fully... Read more
the first lecture of Elements of Data Science and Artificial Intelligence will be held tomorrow (21st of October) at 12:15 by Prof. Jens Dittrich. Like all of Prof. Dittrich's lectures, it will be streamed to Youtube only, i.e. the lecture is fully online. The links to the Youtube streams of the lectures can always be found in the materials section or in the respective timetable entry shortly before the lecture. The organization of the lectures is also documented on the organization page of the course.
Furthermore, you are now able to set your tutorial preferences on your personal status page in the CMS. The deadline for changing your preferences is the 28th of October, 23:59. Shortly after, you will be assigned a tutorial.
Elements of Data Science and Artificial Intelligence
Artificial intelligence is a long-standing branch of computer science concerned with the design of algorithms and systems exhibiting intelligent behavior. Data science is a comparatively young area concerned with the extraction of knowledge and insights from structured and unstructured data. Increasingly, the real power of computer science applications lies in combining the two, exploiting insights from data to take intelligent decisions.
Both artificial intelligence (AI) and data science (DS) are complex multi-disciplinary scientific fields. This course provides an overview of central concepts and ideas, structured and motivated by prominent applications requiring elements from both DS and AI. We start with a brief introduction to machine learning (ML), which lies at the heart of the intersection between DS and AI. We then cover game playing, explaining the search and learning techniques essential to recent successes in Go and Chess. We cover autonomous driving as a prominent application of sensing, system design, control, and learning. We cover dialogue systems and the associated learning and reasoning techniques for natural language processing. We finally cover the data processing techniques required to enable big data.
The aim is for students to understand the scope of DSAI and to obtain intuitions about its central algorithmic elements. Detailed technical expositions and analyses of these elements are not covered; these are the subject of later more specialized courses.
The course is accompanied by exercises, covering technical concepts through examples, as well as posing simple programming exercises (suitable for first-term students) in the Python language. This course also contains a quick introduction to Python.