News
Exam InfoWritten on 07.12.23 by Yue Fan Dear all, The exam for this course will take place on 21st February 2024 at 10:00 at Günter-Hotz-Hörsaal. Please write an email to Fan Yue if you have another exam on the same day. |
Tutorial, office hour, and the first assignmentWritten on 30.10.23 by Yue Fan Dear all, The assignment sheets will be handed in groups of two or three students. Please register your team grouping on the CMS, and the deadline is 10th November. The first tutorial starts from next week. The tutorials of the first two weeks will be about installing the VMs, you can choose to… Read more Dear all, The assignment sheets will be handed in groups of two or three students. Please register your team grouping on the CMS, and the deadline is 10th November. The first tutorial starts from next week. The tutorials of the first two weeks will be about installing the VMs, you can choose to come at either one. Our office hour sessions will start next week as well (E1.3 SR016 Thu 10-12 and E1.1 SR206 Tue 12-14). |
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Written on 23.10.23 by Yue Fan Dear all, The tutorial timeslots have been updated. Please set or update your tutorial preference on your personal status page in the CMS. The deadline is 6th November. You will be assigned a tutorial shortly afterward. |
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.