Practical Lab SS 19 Practical Lab Numerical Simulation
Algorithms in Machine Learning and Their Application
There are no lectures on July 3rd and July 10th.
The presentations will be on July 17th and July 18th. The schedule is:
On July 17th:
- 14.00 Group A
- 14.20 Group H
- 14.40 Group C
- 15.00 Group F
- 15.20 Group J
On July 18th:
- 14.00 Group I
- 14.20 Group D
- 14.40 Group B
- 15.00 Group E
- 15.20 Group K
Additional information (see also the end of sheet 6) regarding the presentation:
- Bring your own laptop or send the presentation as a PDF.
- The 10 minute time limit is very strict. We will give you a sign after 5min and 9min.
- If you picked a dataset/task from sheet 6 only describe it very briefly.
- Send in your code and the presentation.
- The sheets can be found in the left side bar or on mobile devices at the top
- A Jupyter notebook tutorial for Python and NumPy, created by Olmo.
- Template and material for sheet 3.
- Template for sheet 4.
- Material for sheet 5.
- The crash crash data, the riverlevel data with template.
Read the river level dataset with
import pandas as pd df = pd.read_pickle("riverlevels.pandas.pickle")
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.