Practical Lab SS 18 Practical Lab Numerical Simulation
Algorithms in Machine Learning and Their Application
The exercise sheets can be found in the side navigation. Other material:
- The requirements.txt file with the necessary Python 3 packages
- The Introduction slides from the first meeting
- The Python tutorial (Jupyter notebook) from the first meeting
- The material for sheet 3
- The material for sheet 4
- The handout from the Diffusion Maps lecture
- The template for sheet 5
- The rhine level data set with template
- The car crash data set
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 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.