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

05.12.2019

Assignment 6: Update Virtual Machine

Dear students,

for this weeks assignment and some of the upcoming lectures, you have to update your virtual machine. For this, follow these instructions:

  • download the new Vagrantfile from our materials section
  • copy the new Vagrantfile into your virtual... Read more

Dear students,

for this weeks assignment and some of the upcoming lectures, you have to update your virtual machine. For this, follow these instructions:

  • download the new Vagrantfile from our materials section
  • copy the new Vagrantfile into your virtual machine folder (i.e. replace the old Vagrantfile with the new one)
  • execute the following command: vagrant up --provision-with miniconda,python-packages
  • note: the update might take some time

In case you have problems, please use the forum.

Best regards,
Joris

25.11.2019

Accessibility of course materials

Dear students,

please note that the course materials about autonomous driving will only be accessible while logged into the CMS.

Best regards,
Joris

15.11.2019

Assignment 2: Correction of Points

Dear 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.
We are sorry for any inconveniences.

Best regards,
Joris

31.10.2019

Assignment 1

Dear students,

we just uploaded Assignment 1 in the materials sections. The assignment is a zip file consisting of a Jupyter Notebook and a Python file containing the unit tests. You have to move both files into your virtual machine to work on the assignment. In... Read more

Dear students,

we just uploaded Assignment 1 in the materials sections. The assignment is a zip file consisting of a Jupyter Notebook and a Python file containing the unit tests. You have to move both files into your virtual machine to work on the assignment. In addition, please consider the following points:

  • Upload your solutions to the CMS under your personal status page in groups of three students until November 7, 2019 12:00 noon. You can upload your solutions as often as needed. Only the last submission counts.
  • Make sure that your submission contains the names and matriculation numbers of all team members.
  • Exactly one team member should upload the solution.
  • Your submission should only contain the Jupyter Notebook assignment01.ipynb
  • Late submissions will not be graded.

Use our forum to discuss the exercises, ask questions, and help your fellow students. However, sharing complete solutions is not allowed.
 

Best regards,
Joris

25.10.2019

Introductory lectures Python

Dear students,

next week on Monday, 28 October 2019, and on Thursday, 31 October 2019, we will have the introductory lectures for Python. The lectures will contain a hands-on part, where you can try out the principles and and techniques shown to you.
In order to... Read more

Dear students,

next week on Monday, 28 October 2019, and on Thursday, 31 October 2019, we will have the introductory lectures for Python. The lectures will contain a hands-on part, where you can try out the principles and and techniques shown to you.
In order to participate, please bring your laptop to the lectures and make sure that the systems (VirtualBox, Vagrant) are working.

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,
Joris

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

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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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