News
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05.12.2019
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Assignment 6: Update Virtual MachineDear students, for this weeks assignment and some of the upcoming lectures, you have to update your virtual machine. For this, follow these instructions:
Dear students, for this weeks assignment and some of the upcoming lectures, you have to update your virtual machine. For this, follow these instructions:
In case you have problems, please use the forum. Best regards, |
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25.11.2019
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Accessibility of course materialsDear students, please note that the course materials about autonomous driving will only be accessible while logged into the CMS. Best regards, |
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15.11.2019
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Assignment 2: Correction of PointsDear students, we just noticed that assignment sheet 2 has a total of 40 points. Since we want all assignments to have 20 points, we will halve the points of each exercise. Best regards, |
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31.10.2019
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Assignment 1Dear students, Dear students,
Use our forum to discuss the exercises, ask questions, and help your fellow students. However, sharing complete solutions is not allowed. Best regards, |
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25.10.2019
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Introductory lectures PythonDear students, Dear students, Note: The lecture hall E1 3, 002 only contains a limited number of power supply socket at the sides. Therefore, make sure to charge your laptop in advance. Best regards, |
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21.10.2019
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Laptop required for Assignment Sheet 0Dear students, please bring your laptop to your tutorial to be able to work on assignment sheet 0. The assignment sheet deals with the installation and usage of Vagrant, Virtualbox, and Jupyter Notebook. Since this setup requires a stable Internet connection and... Read more Dear students, please bring your laptop to your tutorial to be able to work on assignment sheet 0. The assignment sheet deals with the installation and usage of Vagrant, Virtualbox, and Jupyter Notebook. Since this setup requires a stable Internet connection and takes some time, we recommend to do this setup beforehand. A guide on how to setup the systems can be found on our instructions page. Best, |
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21.10.2019
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Tutorial Assignment and Assignment 0Dear students, we just assigned you to your tutorials. You can check your tutorial slot on the personal status page. Furthermore, the very first assignment sheet will be released today, at 12:00 noon. This assignment sheet covers the installation and usage of... Read more Dear students, we just assigned you to your tutorials. You can check your tutorial slot on the personal status page. Furthermore, the very first assignment sheet will be released today, at 12:00 noon. This assignment sheet covers the installation and usage of the systems we will use throughout the lecture. The first tutorials will be held this week already. They are intended to get acquainted with your tutor and your fellow students. In addition, you can start working on the first assignment. The location of all tutorials is the seminar room 3.06 in building E1 1. Best, |
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.