Machine Learning: Unsupervised Methods (with Problem Based Learning)
Prof. Dr. Laurenz WiskottLecturer
|(+49) 234-32-27997 email@example.com NB 3/29|
Pavlos Rath-Manakidis, M.Sc.Teaching Assistant
|(+49)234-27988 firstname.lastname@example.org NB 3/35|
Aya Altamimi, M.Sc.Teaching Assistant (primary contact)
|(+49)234-27987 email@example.com NB 3/70|
Robin Schiewer, M.Sc.Teaching Assistant
|(+49) 234-32-27988 firstname.lastname@example.org NB 3/35|
- Course type
- Project seminar
- 9 CP
- Winter Term 2022/2023
- moodle course available
- Project seminar
every week on Tuesday from 10:30 to 13:45 in room online.
First appointment is on 11.10.2022
Last appointment is on 31.01.2023
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.
- 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.
1st exam date and time: The exam will be a 90 minutes in-person exam, taking place at ID 03/139 CIP pool, and will be available on Exam Moodle (https://online-exam.ruhr-uni-bochum.de/course/view.php?id=555) between 11:00am-13:00pm (120 minutes time slot) on the 28th of February 2023. Students don't need to start the exam at 11:00am sharp, but will need to make sure that they start no later than 11:10am. The exam will end and your answers will be submitted automatically when the 90 minutes are over.
2nd exam date and time: The exam will be a 90 minutes in-person exam, taking place at ID 03/139 CIP pool, and will be available on Exam Moodle (https://online-exam.ruhr-uni-bochum.de/course/view.php?id=555) between 11:00am-13:00pm (120 minutes time slot) on the 28th of March 2023. 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).
The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210