New Policy on LLMs and Transparency in Work Creation

We are implementing a new policy to ensure transparency and clarity in the creation of student works, especially those involving Large Language Models (LLMs) like GPT. This policy applies to all works, including seminar papers, thesis, and project reports, and is designed to foster academic integrity, rigor, and replicability in your research.

Key Changes:

  1. All works must present a clear and transparent account of the methods and tools used in their creation. This includes documenting any use of AI or LLMs in the process.
  2. When LLMs are used, students are required to submit the complete chat history. This includes both the prompts given to the LLM and its responses. Students should ensure that they use a single chat session, save it in its entirety, and send it separately along with their work. This will provide a comprehensive record of how the work was generated.
  3. In the methods chapter of your work, there must be a specific sub-chapter detailing the tools and methods used to support your writing and study. This is to ensure that all methods, including AI-based tools, are fully explained and their role in the creation process is clear.

Winter 24/25

Sustainability with Machine Learning

Lecture and Tutorial

Sustainability with Machine Learning (Sust-ml)

Lecturer:
  • Prof. Dr. Hannes Rothe
  • Mahnoor Shahid, M.Sc.
Contact:
Term:
Winter Semester 2023/2024
Cycle:
Each Winter semester
Time:
Lecture: Fridays , 10:00 - 14:00
Room:
R09 H04 S02
Start:
11.10.2024
End:
31.01.2025
Language:
English

Important Notes:

The first lecture will take place on Friday, October 11, 2024, from 10 AM to 2 PM in Room R09 H04 S02.

Please fill in this form if you intend to attend this course: https://forms.gle/nJ9UhByurNm2q4J57

Description:

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.

Learning Targets:

  • Develop an understanding of sustainability principles and their application in technological advancements.
  • Learn about 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.

This course allows you to collect credits for the Sustainable Education Certificate (BNE)

Outline:

  • 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

Literature:

  • Sustainability: A Comprehensive Foundation (2015) by Tom Theis and Jonathan Tomkin (eds.)
  • Introduction to Sustainable Development (2012) by Jennifer A. Elliott 2012
  • Pattern Recognition and Machine Learning (2006) by Christopher M. Bishop
  • Machine Learning Yearning (2018) by Andrew Ng
  • Deep Learning (2016) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  • Sustainable Supply Chains: A Research-Based Textbook on Operations and Strategy (2017) by Yann Bouchery, Charles J. Corbett, and Jan C. Fransoo
  • Predictive Analytics for Energy Efficiency Improvement (2019) by Sime Curkovic and Amir S. Gandomi
  • Environmental Monitoring Handbook (2002) by Frank R. Burden and Robert A. McDonnell
  • Artificial Intelligence for Good: How Technologies Can Save Our World by Rajiv Malhotra

Methods of Assessment:

Written Exam (50%) and Course Report (50%)

Teaching

We conduct research on the intersection between digital entrepeneurship, digital ecosystems, and organizing data and knowldedge. We will provide multiple courses that help to explain changes on firm- and ecosystem levels using multiple theories and methodologies. Our teaching is usually highly interactive and builds upon concepts of blended learning, oftentimes 'flipping the classroom'. It is important to us to not only reflect latest findings of information systems research with our students but also to work together on applying that knowledge. 

Our teaching extends to Bachelor, Master, PhD and Postdocs. Offers for PhD and PostDocs will be more clearly defined in the following months.