Machine Learning - Interactive curriculae of TU Ilmenau

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module properties Machine Learning in degree program Bachelor Data Science 2025
module number	201313
examination number	220506
department	Department of Computer Science and Automation
ID of group	2252 (Data-intensive Systems and Visualization)
module leader	Prof. Dr. Patrick Mäder
term	winter term only
language	Englisch
credit points	5
on-campus program (h)	45
self-study (h)	105
obligation	obligatory module
exam	examination performance with multiple performances
details of the certificate	Das Modul Machine Learning mit der Prüfungsnummer 220506 schließt mit folgenden Leistungen ab: alternative semesterbegleitende Prüfungsleistung mit einer Wichtung von 50% (Prüfungsnummer: 2200900) alternative semesterbegleitende Prüfungsleistung mit einer Wichtung von 50% (Prüfungsnummer: 2200901) Details zum Abschluss Teilleistung 1: one or multiple written tests consisting of multiple-choice and free-form questions evaluating the professional competence in the course's topics preferably conducted digitally via Moodle and on the students' personal devices final results may be scaled or individual questions may be excluded depending on statistical analysis of the results Details zum Abschluss Teilleistung 2: examination via practical, e.g. coding, assignments to be conducted in person in class or at home evaluating methodological and practical competence potentially presenting the results in class
link to Moodle course
teacher	Prof. Mäder
signup details for alternative examinations	This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer.
maximum number of participants
previous knowledge and experience	. Fundamentals of mathematics (linear algebra, analysis), probability theory . Basic Python programming skills
learning outcome	Professional competence gained through lectures and examined through written exams: Students have knowledge and can demonstrate knowledge in . - the basic concepts of machine learning, especially supervised learning - mathematical foundations and algorithms for machine learning, including k-nearest neighbors, logistic regression, Gaussian processes and decision trees - selecting and applying model validation techniques such as cross-validation and under/overfitting avoidance - conducting and adapting the process of model evaluation using metrics such as accuracy, precision, recall and F1 score Methodological competence gained through seminars and examined through practical evaluation: Students have the ability . - to apply data pre-processing methods such as normalization and feature engineering - to apply machine learning algorithms to real-world datasets and interpret their performance - to use common machine learning libraries such as Scikit-learn Social competence gained through lectures and seminars: - Students have insights in ethical aspects of machine learning (e.g., bias, autonomous driving) through discussions in lectures and seminars. - Students can discuss advantages and disadvantages of different machine learning comcpets among each other and with their lecturers and gained experience in mastering discussions beyond their mother tongue
content	The topics covered include: . Introduction to machine learning (supervised, unsupervised, reinforcement learning) . Linear regression and classification models . Decision trees and random forests . Clustering algorithms (k-means, hierarchical clustering) . Model validation and evaluation (cross-validation, ROC curves) . Optimization techniques . Use cases in image processing, text analysis and time series analysis
media of instruction and technical requirements for education and examination in case of online participation	presentations, homework task sets as PDF, assignments including code stubs, Jupyter notebooks
literature / references	"Pattern Recognition and Machine Learning" von Christopher M. Bishop "An Introduction to Statistical Learning" von G. James, D. Witten, T. Hastie, R. Tibshirani, J. Taylor "Machine Learning with PyTorch and Scikit-Learn" von S. Raschka
evaluation of teaching