Winter Semester 2018/19
The P2E1 practical lab for bachelor students is already full (there are still places available for master students) and no further registrations can be accepted. Please note that this semester there is also another P2E1 practical lab offered by Prof. Dr. Marc-Alexander Schweitzer. Besides, this practical lab will also be offered next semester.
Please send an e-mail to the registration address above. If the number of registrations exceeds our limit, we will randomly choose the participants among all registrations. We will do so in three steps: First all registrations before August, 17th, are considered. Then, all registrations before October, 1st. Finally, if there are any capacities remaining, all registrations after that date are considered.
Nowadays, data mining and machine learning algorithms are the backbone of decision making processes in all major enterprises. Their applicability seems almost endless and ranges from selective advertising over prototype design to autonomous production chains. Due to the availability of very large datasets (“Big Data”) it has become crucial to understand the mechanics of the different types of learning methods and to be able to develop and implement efficient algorithms to meet the requirements of the task at hand.
In this practical lab, we teach the basic mathematical and technical tools needed to understand a range of basic data mining and machine learning methods. A strong emphasis is put on algorithms and efficient implementation.
Roughly every two weeks a new practice sheet is given to the participants. The tasks will be worked on in small groups. The time needed will be six hours a week.
Basic experience in Python is a necessary requirement. Further, the Python packages Numpy and Matplotlib will we used. The corresponding websites provide introductions which are sufficient for our purposes. All programming tasks are done using Jupyter notebooks. For the case of having no experience in the mentioned tools we recommened to spend a little time familiarizing yourself with these before the course starts.