Customise your own sustAIn.brussels training track !
Tracks oriented towards management:
Sustainable digitalization & IT
Sustainable digitalization
Strategizing for the Sustainable Transformation of (your) organization
1. Description
The aim of this course is to introduce participants to the complex relationships between digital technologies and sustainability, considering the environmental, societal and economic pillars. Besides demystifying such relationships, the objective is also to teach participants how this translates practically in organizations’ strategies and operations. To do so, the teachers will rely on both theoretical and practical sessions, where guest lecturers will provide testimonials on how they dealt with such issues and what were the enablers and obstacles in such transformation. Regular and interactive exercises will also be included in the training to allow for participants to immediately reflect on the issues and be able to apply theoretical and practical concepts to their reality. Finally, this course will end up with a cocreation workshop aiming at developing a strategic framework by and for SMEs to navigate such dual transformations.
2. Course content & learning outcomes:
Course content
• Introduction to the concepts of digitalization/digital transformation & sustainability/sustainable development
• Introduction to the complex relationships between digital technologies and sustainability
• Strategy development integrating digital transformation and sustainability
• Organizational, operational and communicational practices
• Practical testimonials
Learning outcomes:
• Improve knowledge on digital & sustainability issues at large
• Understand practical, business implications of such high-level issues
• Strategically apply such knowledge to own realities
• Gain insights on best practices from the field
3. Teachers :
Sustainable IT
Implementing digital technologies sustainably and responsibly
1. Description
The digital transformation of companies goes hand in hand with an aspiration to become more sustainable. As both transformations may change the heart of the business model, making digitalization and sustainability aspirations align can form a key challenge. This course aims to introduce professionals within the business domain to a nuanced, technology-oriented and pragmatic view of the sustainable implementation of digital technologies. The course will provide expert perspectives on cybersecurity and responsible IT practices, and participants will gain an in-depth understanding of the intricate interplay between digital technologies, and environmental and social sustainability in a corporate context. Here, you will gain insight into quantitative metrics essential for viewing sustainability but also show the alignment with broader organizational objectives, both in terms of digital transformation and sustainability (reporting) goals. Participants can expect an interactive course format, integrating online modules with a live workshop.
2. Course content & learning outcomes:
Course content
• Introduction to the interlinkage between digitalization and sustainability
• Linking sustainable IT with broader corporate sustainability and reporting
• Measuring is knowing: what tools can you use to measure the sustainability impacts of IT
• Use cases and business-oriented applications of concepts
Learning outcomes:
• A pragmatic, technology-oriented view on sustainable digitalization in corporate environments
• Gain an intuition for linking digital technologies with their (often invisible) sustainability impacts
• Gain knowledge of available tools to measure and report impacts
• Gain insights on best practices from the field
3. Teachers :
- Arjen van de Walle
- Yann-Aël LE BORGNE
- Dimitris Sacharidis
Registration Sustainable Digitalization & IT
Introduction to Data Science
From Raw Data to Actionable Insights
1. Description
Dive into the world of data-driven decision-making with our comprehensive Data Science course. Designed for professionals from diverse backgrounds this course equips you with essential skills to harness the power of data for insightful analysis and predictive modeling.
In the first part of the course, we will provide solid theoretical bases, introducing core concepts in data science such as data types, data formats, data quality issues and predictive modeling. In order to facilitate application in practical settings, these concepts will be directly related to existing use cases under the supervision of the course lecturers. For the second part of the course, we will employ industry standard Python packages (e.g. pandas, numpy, scikit-learn), to develop a prototype of a data analysis pipeline. Participants are assumed to already have a working knowledge of programming (ideally Python), but additional self-learning resources will be provided if needed.
2. Course content & learning outcomes:
Course content
● Introduction to Data Science and Data Analytics
● The four flavors of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
● Implementing a data analysis pipeline with Python
● Data Science in Practice: Do’s, Dont’s and Ethical considerations
Learning outcomes:
By the end of this course you should be able to:
● Explain the core concepts involved in a Data Science project (i.e data types, data storage, data analysis, predictive modeling);
● Examine an existing situation to identify what suitable Data Science methodologies and analysis are applicable
● Develop a prototype of a data analysis pipeline using Python and its data science toolkit (pandas, seaborn, sci-kit learn, …)
3. Teachers :
- Jacopo DE STEFAN
- Jean CARDINAL
- Yann-Aël LE BORGNE
Registrations closed
register your interest for introduction to datascience
Artificial intelligence
Explore how custom-made AI can be key to your sustainable digitalization
1. Description
This course delves into the principles of AI and the impact it hason society, using practicalfields (eg: healthcare, biomechanics) and novelties as examples. From AI ideation to AIapplications, you will gain significant insights on the impacts and opportunities AI will have onyour business and on society through its societal dimension.
2. Course content & learning outcomes:
Course content
- Introduction: AI: what is it and what it can do?
- AI applications
- Legal and ethical aspects of AI
- Sociological aspects of AI
Learning outcomes
- Have an overview of the impact AI has insociety.
- Discover the application of AI with specific topics, such as health, biomechanics, law, ethics and sociology
3. Teachers :
- Giovanni BRIGANTI
- Bernardo INNOCENTI
Registration to Artificial intelligence
Tracks oriented more technically:
Machine learning
Demystifies machine learning algorithms tools & gets you to apply them with Python
1. Description
The aim of this course is to introduce the basic concepts of machine learning. We will have botha theoretical part with slides and a practical part where we will use the Python programminglanguage for the exercises. In the theoretical part, we will introduce concepts such assupervised, unsupervised and reinforcement learning. A few examples of classic algorithms willbe presented, such as tree-based models, naive Bayesian classification and neural networks.Without seeking to formalise too much, the theoretical aspects of machine learning will bepresented both intuitively and using a mathematical formalism. For the programming part, thebasic Python packages used in machine learning will be presented, but participants will beassumed to already have a good command of programming (ideally Python). By the end of thecourse, the aim is to have contributed to demystifying the tools used by data scientists and, inthe advanced track, to have manipulated simple models on real data.
2. Course content & learning outcomes:
Course content
- Introduction to Machine Learning from Scratch
- Simple machine learning models
- The basics of deep learning
- Machine learning with Python
Learning outcomes:
- Improve your knowledge and use of Python;
- Gain understanding of models and tools used by data scientists;
- Acquire a solid foundation in machine learning to pursue studies in the field ;
- Put your theoretical knowledge in programming into practice
- Gain confidence to apply and manipulate simple p
- rogramming models on real data
3. Teachers :
- Olivier CAELEN
- Yann-Aël LE BORGNE
- Jacopo DE STEFANI
register your interest for Machine learning
ChatGPT & Next-generation assistants
Identify the strengths and limits of next generation chatbots for your business
1. Description
The coursewill aim at explaining the nuts and bolts of the new generation of chatbots such asChatGPT, what they can be used for, and what are their limits. We will start with an overviewof reference chatbots (both closed and open source, ChatGPT and Mistral for example),common use cases (writing, summarizing, brainstorming, ...), and some of the main limits ofthese tools (bias, hallucinations, ...). The course will then dive gradually into more advanceduse cases, addressing along the way more subtle issues, bothethical and technical. Examplesof ethical issues that we will address cover data privacy, intellectual property, and the digitaldivide. On the technical side, we will show how chatbots can be programmed and trained, usingeither APIs for closed-source solution, or libraries such as HuggingFace for open-sourcemodels. By the end of the course, the goal is that participants can make their informed decisionson the capabilities of this new type of software, and when and for what they may appropriatelyuse them.
2. Course content & learning outcomes:
Course content
- Introduction to large language models (LLMs) such as ChatGPT
- Experiment with different use cases
- Discuss about limitations, both technical and ethical
- Program LLMs, both closed and open-source models (workshop day-technical profile)
- Design a Chatbot Web application (workshop day-technical profile)
Learning outcomes:
- Demonstrate an understanding of large language models' technology within the field of machine learning
- Identify the primary use cases for LLMs and formulate effective prompts
- Recognize and explain the technical limitations and ethical issues associated with LLMs
- Use and program with LLMs in Python
- Apply methods for data augmentation and fine-tuning
3. Teachers :
- Yann-Aël LE BORGNE
- Olivier CAELEN
Register your interest for ChatGPT & Next-generation assistants
