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Modul (6 Credits)
Sustainability with Machine Learning
- Name im Diploma Supplement
- Sustainability with Machine Learning
- Verantwortlich
- Voraussetzungen
- Siehe Prüfungsordnung.
- Workload
- 180 Stunden studentischer Workload gesamt, davon:
- Präsenzzeit: 60 Stunden
- Vorbereitung, Nachbereitung: 60 Stunden
- Prüfungsvorbereitung: 60 Stunden
- Dauer
- Das Modul erstreckt sich über 1 Semester.
- Qualifikationsziele
Students will be able to
- Assess use cases for machine learning within business environments
- Develop an understanding of sustainability principles and their application in technological advancements.
- Apply data preparation procedures, machine learning algorithms, and methodologies for training and evaluating models.
- Explore deep learning architectures, including Vision and NLP models.
- Discover how supply chain management, environmental monitoring, energy efficiency can be improved with the help of machine learning.
- Recognise the ethical issues involved and how AI should be applied fairly in sustainability applications.
- Prüfungsmodalitäten
This module is assessed based on three grading instruments:
- First, a series of 4 assignments during the semester, that students must pass to be allowed to take the final examination.
- Second, a final project presentation (30% of the grade) and a final project report of their case study (30% of the grade).
- Third, a final written examination (usually 60 minutes, 40% of the grade)
- Together, written examination and final project result in the course grade. The specific formalities will be announced in the first session.
- Verwendung in Studiengängen
- Bestandteile
Vorlesung (3 Credits)
Sustainability with Machine Learning
- Name im Diploma Supplement
- Sustainability with Machine Learning
- Anbieter
- Lehrperson
- SWS
- 2
- Sprache
- englisch
- Turnus
- Wintersemester
- maximale Hörerschaft
- unbeschränkt
- empfohlenes Vorwissen
The students should have a basic knowledge of information systems and be familiar with:
- Programming languages such as Python. Good to know frameworks like Pytorch or Tensorflow (but not mandatory).
- Mathematics and Statistics. Topics such as differentiation, matrix operations, vector spaces, and basic probability distributions will be relevant.
- Data Analysis and Manipulation. Basics of data cleaning, transformation, and data analysis.
- Abstract
Sustainability with Machine Learning explores the integration of machine learning techniques into sustainability domains to address environmental and social challenges. This course covers the foundations of machine learning, deep neural networks, and sustainable development applications. Students will gain knowledge of how machine learning may increase sustainability in decision-making, facilitate environmental monitoring, better supply chain management, and optimize energy efficiency. This course also reflects on using AI fairly and with ethical considerations in order to promote sustainable practices.
- Lehrinhalte
- Introduction to Sustainable Development
- Fundamentals of Machine Learning
- Deep Learning Architectures
- Sustainable Supply Chain Management
- Predictive Analytics for Energy Efficiency
- Environmental Monitoring and Conservation
- Ethical and Fair AI for Sustainability
- Literaturangaben
- Sustainability: A Comprehensive Foundation by Tom Theis and Jonathan Tomkin (eds.)
- Introduction to Sustainable Development by Jennifer A. Elliott
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Machine Learning Yearning by Andrew Ng
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Sustainable Supply Chains: A Research-Based Textbook on Operations and Strategy by Yann Bouchery, Charles J. Corbett, and Jan C. Fransoo
- Predictive Analytics for Energy Efficiency Improvement by Sime Curkovic and Amir S. Gandomi
- Environmental Monitoring Handbook by Frank R. Burden and Robert A. McDonnell
- Artificial Intelligence for Good: How Technologies Can Save Our World by Rajiv Malhotra
Further literature will be provided during the course
- didaktisches Konzept
There will be lectures in a traditional way, but students will have the opportunity to critically reflect on recently learned material during class discussions and engage with the lecturer in open discussion, enabling active student participation. Problem solving exercises along with some short practical tasks will be provided as assignments to the students in a student-centered approach where each student can assess their understanding of different topics. For more hands-on-experience and collaborative learning, there will be project-based learning from the mid of the semester in which students will work on an AI project in small teams which may culminate in presentations, reports, or prototypes.
- Hörerschaft
Übung (3 Credits)
Sustainability with Machine Learning
- Name im Diploma Supplement
- Sustainability with Machine Learning
- Anbieter
- Lehrperson
- SWS
- 2
- Sprache
- englisch
- Turnus
- Wintersemester
- maximale Hörerschaft
- unbeschränkt
- empfohlenes Vorwissen
See lecture
- Abstract
See lecture
- Lehrinhalte
See lecture
- Literaturangaben
See lecture
- didaktisches Konzept
The conceptual structure of these tutorials focuses primarily on assisting in assignments, development of the project, emphasize teamwork, group discussions, and presentation sessions.
- Hörerschaft