Overview of the Course
What is the subject context of the course?
This course is offered in the Department of Mechanical Engineering, following the «Introduction to Computer Science» and «Computer Science II» lectures. It was developed in response to the department’s desire to integrate statistical and probabilistic thinking into the curriculum, alongside machine learning. The course is structured in two interconnected parts: one focusing on stochastic methods and the other on machine learning. The aim is to bridge statistical theory with machine learning.
What should students learn and be able to do at the end of the course?
Students should be able to apply probability theory and statistical methods in the context of machine learning. Beyond theoretical understanding, they are expected to gain hands-on experience with machine learning tools and techniques—building on the lecture «Computer Science II».
Why was the specific assessment format chosen?
The assessment is split into 2 project based submissions and a digital examination. Because hands-on experience with machine learning is difficult to assess through traditional exams, students complete practical projects. These projects are implemented using Google Colab and Kaggle for project 1, and for project 2 using various computer platforms such as JupyterHub provided by ISG (Informatic support group), Google Colab, Kaggle and openreview.net. The digital exam at the end of the semester assesses both the programming component and theoretical understanding of stochastic processes, probability theory, and machine learning. It consists of Moodle-based questions and stochastic tasks in Code Expert. Additionally, students may earn up to 0.25 bonus points on the digital exam based on their performance in weekly exercises submitted via Code Expert.
How are students prepared for the assessment?
Preparation is provided through weekly exercises, detailed feedback, and supporting lectures.
Shared Experience
How many times has the assessment been conducted in this format?
The current assessment format has been implemented twice. In the spring semester of 2024, the course included two coding projects with automated grading of the results alongside the digital exam. In spring 2025, the format was expanded to require a short report which was manually reviewed as part of the second project.
What contributed to the success?
The course was attended by a large number of students, which made manual grading impractical for the final exam, therefore the use of digital tools (specifically the combination of Moodle and Code Expert) proved to be an ideal solution for the final exam.
What were the challenges and how were they overcome?
One major challenge was the volume of content, which needs further adjustment to better align with the scope of a 5-credit course.
The project based submissions placed a significant workload on the teaching team. Particularly each group submission in project 2, which included detailed project reports from teams of two to three students, had to be carefully reviewed and provided with feedback. Still it was important to give students a more detailed report on their work for the second project that covered further aspects beyond the purely technical performance of their code.
For the digital exams, designing effective questions was key. Open-ended questions had to be translated into multiple-choice or calculation-based formats to enable automated grading. The grading system was kept conservative, especially regarding accepted answer ranges, to ensure accuracy.
Are there any further developments planned?
A reduction in content volume is planned, while the project-based format will be retained. There is also an intention to integrate even more components into Code Expert.
What tips would you give to lecturers planning a similar assessment?
Thorough preparation is essential, especially when designing exam questions that do not require manual correction.
It is also crucial to hire a sufficient number of doctoral teaching assistants to support the evaluation of project work, which requires careful supervision by the lecturers. Office hours to support students with their project work beyond regular lectures and exercises were greatly appreciated by the students.