Credits: 9 CP
Workload: 270 h
Semester: any semester Master
Cycle (Turnus): each WS
Duration (Dauer): 1 semester
Contact time (Kontaktzeit): 4 SWS (60 h)
Self studies (Selbststudium): 210 h
Group size (Gruppengröße): ca 40
Teaching format (Lehrformen): This course is given in a hybrid of inverted classroom and problem based learning. The course starts with a two-week introduction into unsupervised methods of machine learning, providing an overview. The students then work in groups of about 4 on realistic problems that can be solved with these methods. In the first week of a problem, they develop hypotheses and strategies for a solution and identify which methods they want to learn. Then the course agrees on a method to focus on theoretically, which will then be done in an inverted classroom format. The students then try to solve the problem and present their results in a short video talk with slides. Thus the students will not only learn about machine learning but also soft skills.
Requirements: The mathematical level of the course is mixed but generally high, including calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...). Programming is done in Python, thus the students should have a basic knowledge of that as well, or at least be fluent in another programming language.
Content (Inhalt): This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, Bayesian theory and graphical models.
Learning outcomes (Lernziele):
After the successful completion of this course the students
- know a number of important shallow unsupervised learning methods,
- can discuss and decide which of the methods are appropriate for a given data set,
- understand the mathematics of these methods,
- know how to implement and apply these methods in python,
- have gained experience in organizing and working in a team,
- know problem solving strategies like brain storming,
- can communicate about all this in English.
Exam (Prüfungsformen): The exam is a combination of graded presentations for the problems and graded quizzes for the theory. 50% of the grade come from the average group performance on solving the problems. 10% come from the presentations, taking into account slides and presentation style, this is an individual grade of the presenter. 40% come from a digital quiz about the theory of the methods covered. Thus 50% of the grade are individual, 50% come from the group. In addition you can gain up to 4 bonus points for being voted for as a 'most valuable player (MVP)' on a project. Since the exam is distributed over the semester, students (at least of Applied Computer Science) must register for it at the beginning of the semester.
Condition for granting the credit points (Voraussetzungen für die Vergabe von Kreditpunkten): Continuous participation and passed exam.
Exam date and time: Info from last year, to be updated in due time: The exam will be a 90 minutes online exam, available on Exam Moodle (https://online-exam.ruhr-uni-bochum.de/course/view.php?id=274) between 11:00am-13:00pm (120 minutes time slot) on the 8th of March 2022. Student don't need to start the exam at 11:00am sharp, but will need to make sure that they start no later than 11:30am as the exam will end for all students at 13:00pm sharp.
Retry exam date and time: Info from last year, to be updated in due time: The exam will be a 90 minutes online exam, available on Exam Moodle (https://online-exam.ruhr-uni-bochum.de/course/view.php?id=407) between 11:00am-13:00pm (120 minutes time slot) on the 22th of March 2022. The same time rules of the original exam applies here as well.
Remarks on online Sessions:
- It is really annoying to talk to an array of black tiles. So please turn on your video.
- If you do not want to turn on your video, please at least upload a portrait image of yourself, so that we can see your face. That is better than a black tile.
- Do not use other images, like a black cat, terminator, or a galaxy. Feel free to do that with your buddies, but I consider that inappropriate in this university teaching context.
- I will record some of the online sessions, for the benefit of those who cannot attend. I will tell you beforehand and you will see an indication of it in zoom. Please turn off camera and mic, if you don't want to be recorded (but remember the portrait image).