Elements of Data Science and Artificial Intelligence Prof. Dr. Vera Demberg, Prof. Dr. Jens Dittrich, Prof. Dr. Jörg Hoffmann, Prof. Dr. Bernt Schiele Basic Lecture, 9 CP, Winter Semester 2019

News

21.10.2019

Laptop required for Assignment Sheet 0

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... 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,
Joris

21.10.2019

Tutorial Assignment and Assignment 0

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... 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,

Joris

 

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.



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