ML I, Week 9: Programming Assignment

We have covered in the Machine Learning Class the main learning tasks density estimation, classification, and regression. In this assignment, you are asked to implement a neural net for regression and/or classification and to apply it to data from a realistic problem (e.g., drug design, biotechnology, or finance) of your choice. Select an appropriate model using (cross-)validation.

Document the following:

  1. A description of your implementation of regression/classification, and model selection. (What technique did you implement? Why did you choose that technique?). Write about 10 sentences.
  2. A description of your data (What kind of data did you choose? Why those? How many data points and dimensions? Is this data set used by other people?) Write about 5-10 sentences.
  3. A discussion of your application of the ML techniques to the data (What are the results? Did you expect these results? How do they compare with results from the literature (if available)? Did you observe any difficulties with the ML technique and/or the data? Which difficulties and how did you solve them?) Write about 3-5 pages.
  4. A printout of your code.
Hand in your report to Sibylle Mueller on Thursday, January 23, in class. Also, send your code (in a single file) by email to Sibylle Mueller ( with subject 'ML programming assignment'. You can work on your own or with a partner.

Important: With this exercise, we intend that you gain experience with the topics discussed in class AND that you learn to document your results carefully, i.e., such that someone else can repeat your experiments. So, please include a bibliography, web links, a discussion about strategy parameter choice and motivation of their choice. Imagine you would publish your results in a scientific journal.

due Thursday, January 23, in class