Problem set 3 (extra): Machine learning methods, Gaussian processes, neural networks

Instructions

• The problem sets can be downloaded from the Files tab, or found in the doc/pub directory in the course repository.
• Problem sets are performed individually. See examination rules on the course web page.
• Students are allowed to discuss together and to help each other when solving the problems and working on projects. However, every student must understand their submitted solution in the sense that they should be able to explain and discuss them in detail with a peer or with a teacher.
• The two notebooks for each problem set contain a number of basic and extra problems; you can choose which and how many to work on.
• Several problems contain tasks that should first be graded via the corresponding Yata Question. Such problems are clearly marked and you must have passed the automatic grading in Yata before handing in your solution to receive credits. Note that you must always submit your final solution via Canvas.
• Other problems will be manually graded. Manual grading is performed in the teacher's python environment, which is based on the conda `environment.yml` file in the course github repo. You cannot use additional python modules as this will cause import errors.

• Important: The problem sets are examined individually via:

  • Hand-in of solution code (Jupyter notebook) via Canvas. It is the code and results in your submitted notebook that is considered to be your hand-in solution. See specific deadlines on the corresponding Canvas Assignments. Note that there are separate Canvas Assignments for basic and extra problems.
  • Code tests for certain (marked) tasks via Yata Questions. You need to have a green check mark on Yata to get the corresponding points. See specific deadlines on the corresponding Yata Questions assignment.
  • Face-to-face discussions with a randomly selected subset of students on the first Monday after the problem set deadline. Selected students will meet with one of the teachers and are expected to answer questions on their code implementation and results. No extra preparation is needed for these discussions apart from familiarity with your own solution. A list of randomly selected students will be published on the course web page around Monday noon. During the afternoon session that same day, students will be called in the numbered order until the end of the list (or the end of the exercise session). You must inform the responsible teacher as soon as possible following the publication of the student list if you can not be physically present at the exercise session (in which case we will have the discussion on zoom).
  • An oral examination (on all aspects of the course) will be arranged with the examiner at the end of the course for students that: (i) do not show up for their discussion slot, or (ii) fail to demonstrate convincing familiarity with their hand-in solutions. A small number of students might also be selected randomly for an oral exam. The oral exm will not provide extra credits but only serves to certify the points reached via the submissions.
• Problem sets that are handed in post deadline will only be eligible for points corresponding to the minimum required for a passing grade (for Chalmers PhD students this corresponds to grade 4). Late hand-ins will be graded in December/January (handed in before Nov. 30th) or August/September (handed in before July 31st).

Before you submit your solution, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel Restart) and then run all cells (in the menubar, select Cell Run All).

Make sure that the run time is smaller than a few minutes. If needed you might have to reduce some computational tasks; e.g. by decreasing the number of grid points or sampling steps. Please ask the supervisors if you are uncertain about the run time.