Practical Lab SS 19 Practical Lab Numerical Simulation
Algorithms in Machine Learning and Their Application
- The sheets can be found in the left side bar or on mobile deviees at the top
- A Jupyter notebook tutorial for Python (09. Apr 2019) and NumPy, created by Olmo.
This summer semester the practical lab is only offered as a master module.
We have no more free places available at the moment. You can still send an e-mail to the contact address above for the case a place becomes available.
Since we had a few questions from PhD students. These can take part in the lecture, the tutorial, and do the practise sheets, but we will not give feedback on the practice sheets.
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. Depending on the technical proficiency, the time needed will be about 6 hours a week.
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.
Basic experience in Python is a necessary requirement. Further, the Python packages Numpy and Matplotlib will be used. The corresponding websites provide introductions which are sufficient for our purposes. All programming tasks are done using Jupyter notebooks. Should you have no experience in the mentioned tools we recommened to spend a little time familiarizing yourself with these before the course starts.