Crypto Finance covers an introduction to the blockchain and its applications to crypto markets and portfolio man-agement. The course builds on the basic concepts of the blockchain technology and Bitcoin. The course then ex-tends to the development of Altcoins and their features. The course concludes with the integration of crypto as-sets into portfolio management.
Key topics covered are:
· Blockchain Technologies
· Consensus Mechanisms
· Forks
· Bitcoin
· Altcoins
· Token Sales
· Crypto Portfolio Management

· The module involves interactive lectures and video podcasts by the students.

After successful completion of the course, students can

Professional competence
· understand the most important concepts of blockchain and Bitcoin.
· understand how to categorize altcoins.
· differentiate between types of consensus mechanisms and forks.
· describe and evaluate token sales mechanisms.
· analyse benefits and risks of crypto assets in portfolio management.
Methodological competence
· describe the key concepts of the blockchain technology and Bitcoin.
· categorize and evaluate altcoins.
· evaluate consensus mechanisms.
· understand the occurrence of forks.
· explain the benefits and risks of crypto assets in portfolio management.
Social competence
· organise learning materials and work in groups.
· discuss the topics and results in the lectures.
Personal competence
· prepare and produce a video podcast.
· “Think critically” i.e., they can explain and evaluate the topics covered in the course

· Students are provided with the lecture slides and supplementary material (e.g., selected journal articles)

This optional module allows for various kinds of research studies. It is closely connected to the five units of the Liechtenstein School of Architecture and is usually part of ongoing research projects. The supervision consists of directing the students towards clear results within a given field of research. The individual study is reviewed within the respective unit.

Urbanism, Architecture & Society:
Do you like writing, or want to gain more experience scientific writing?
Are you interested in learning about and applying methods of urban research?
Want some credit points for that?
Urbanism, Architecture & Society Unit (UASU) is happy to support independent study projects utilizing our methods and approaches, either (1) pursuing questions relevant to our group, or (2) supporting you in finding your own research questions. We can provide topics for independent study related to our research areas in the Alpine Rhine Valley, as well as various locations across the Global South.
We also encourage aligning independent study projects with our other concurrent courses including: Raum & Gesellschaft, Studio Jozi, Urban Theory, Planning & Places, Architectural Theory, or Pro Bono.

Self-defined design or research studies, developed individually or in groups agreed upon with research units and under the guidance of mentors. The size of the module is determined by the respective unit.

After successful completion of the course, students will be able to

Relevant reading will be made available at the beginning of the course. A list of recommended literature will be announced in the course and updated on an ongoing basis.

Minimum 75% compulsory attendance, regular meetings with instructors, continuous assessment, portfolio and final review.
The final grade is calculated according to the weighting of the following components: final submission (80%) and oral presentation (20%).

Individual appointments will be set with the tutor.

You can register for this independent study alone or in a group. For registration and enrolment, please get in touch with Lindsay Howe: lindsay.howe@uni.li

The Advanced Investment Strategies course is designed to provide students with in-depth knowledge and practical
skills in advanced portfolio management and investment strategies. The course is structured into two main parts. The first half focuses on advanced portfolio management and optimization techniques such as improved estimates, Black-Litterman and equal risk contribution portfolios. The second half involves student groups presenting and implementing (in R) advanced investment strategies from selected papers, covering various techniques (e.g. statistical arbitrage) and datasets (such as bonds, exchange rates, crypto currencies or derivatives).

Key topics covered in this course include:

• Advanced Portfolio Management Techniques
• Optimization Methods: Improved Estimates, Black-Litterman, Equal Risk Contribution
• Statistical Arbitrage
• Investment Strategies for Bonds
• Exchange Rate Strategies
• Cryptocurrency Strategies
• Derivative-based Strategies
• Implementation of Investment Strategies using R

• The module involves interactive lectures with exercises to integrate theoretical knowledge with critical analysis skills.
• Case studies are used to discuss the course contents.
• Contemporary scientific publications from Information Systems and Human-Centred Design are discussed in class.

After successful completion of the course, students will

Professional competences

• gain advanced knowledge in portfolio management and optimization techniques.
• understand and apply various advanced investment strategies to different asset classes.
• develop the ability to critically evaluate and implement investment strategies.

Methodological competences

• utilize advanced optimization methods for portfolio management.
• apply statistical arbitrage and other quantitative investment techniques.
• integrate financial theories with practical investment strategy implementation.

Technological competences

• proficiently use R for advanced portfolio management and strategy implementation.
• analyse and manage different datasets using R for investment decision-making.
• leverage technology to enhance investment strategy development and execution.

Social competences

• collaborate effectively in groups to research and present investment strategies.
• communicate complex investment concepts and strategies clearly and concisely.
• engage in constructive discussions and provide feedback on peer presentations.

Personal competences

• develop independent research skills in advanced investment techniques.
• enhance problem-solving abilities in the context of financial data and strategies.
• reflect on the ethical and practical implications of advanced investment strategies.

• Stöckl, S. (2024). Tidy Portfolio Management with R. Available via www.tidy-pm.com.
• Additional selected readings and case studies provided via Moodle

Final written exam

Data Visualisation covers techniques for creating effective data visualisations based on principles from statistics, cognitive science, and graphic design to help analysts and decision-makers understand and explore big data. The course covers eight primary topics:

• Visualising univariate and multivariate numerical data
• Visualising time series data
• Visualising geospatial data
• Visualising networked data
• Visualising high-dimensional data
• Visualising textual data
• Interactive dashboards
• Animations

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• Real-life examples are used to show how the course content can be applied in practice.

After successful completion of the course, students will

Professional competence

• be able to create visualisations that inform business decision making
• recognise the typical challenges of visualising large and complex data sets

Methodological competence

• understand the main concepts, theories, and methods of data visualisation
• be able to use data-visualisation methods to analyse business problems, generate possible solutions, and compare these solutions in terms of their effectiveness and efficiency

Social competence

• discuss challenges and benefits of statistical graphics
• help others in group work

Personal competence

• identify new challenges and independently develop viable solutions
• reflect on their own and others’ visualisations

Technological competence

• be able to create graphs like bar charts, scatterplots, line charts, and heatmaps in R to represent various types of data sets visually
• be able to collect and prepare data before it can be visualised

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).
• Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. US: New Riders.

Modul 3 behandelt zunächst das liechtensteinische und europäische Zahlungsverkehrsrecht, von der PSD über die Regulierung von Zahlungskonten bis zu E-Geld-Instituten. Sodann widmet sich das Modul den liechtensteinischen und europäischen Vorgaben zur Geldwäschereiprävention (Geldwäscherichtlini; Sorgfaltspflichtengesetz).
Im Modul werden zentrale Rahmenverträge des Zahlungsverkehrsrecht – etwa Kreditkartenvertrag, Online-Ban-king-Abrede, Basiskontovertrag etc. – erörtert. Konkret werden das Überweisungsverfahren, das Lastschriftver-fahren sowie Zahlungen mit Debitkarte, Geldkarte und Kreditkarte behandelt. Im Zentrum steht die Zahlungs-diensterichtlinie (PSD) sowie das liechtensteinische Zahlungsdienstegesetz. Darüber hinaus wird die Unterschei-dung von klassischen Zahlungsinstituten und E-Geld-Instituten erörtert.
Mit Blick auf die Geldwäschereiprävention steht das liechtensteinische Sorgfaltspflichtengesetz (SPG) und das eu-ropäischen Richtlinienrecht, insbesondere die Geldwäscherichtlinie, im Mittelpunkt. Dabei wird in den Einheiten auch das eng verwandte Thema der Terrorismusfinanzierung behandelt. Angesprochen werden weiters Schnitt-stellen zur prudentiellen Aufsicht (zB Geldwäschereipräventionskollegien), zum «Foreign Account Tax Compliance Act» (FATCA) sowie zum automatisierten Informationsaustausch (AIA).

Interaktive Vorlesung, eingehende Diskussion unter Einbeziehung der Studierenden, ggf. einzelne Gruppenausar-beitungen.

Die Studierenden können komplexe praktische Frage- und Problemstellungen im Bereich des Zahlungsverkehrs-rechts und der Geldwäscheprävention selbstständig beantworten und fundierte Lösungsansätze entwickeln. Die Studierende können Zahlungsdienste rechtlich einordnen und verstehen die zivilrechtlichen Grundlagen des Zah-lungsverkehrs. Im Bereich der Geldwäsche kennen die Studierenden die einschlägigen Sorgfaltspflichten. Sie wis-sen, wie Verdachtsmeldungen im Bereich der Geldwäsche und Terrorismusfinanzierung vorgenommen werden und können präventive Massnahmen und Compliance für Banken im Bereich der Geldwäscherei vorbereiten und betreuen.

Die Literatur wird im Rahmen der Lehrveranstaltung und in den Modulplänen bekannt gegeben.

• This course provides a comprehensive introduction to the principles and practices of academic writing. It focuses on developing the skills necessary for producing clear, well-structured, and scholarly texts. Students will learn to navigate and adhere to academic standards, ensuring their work meets the rigorous expectations of the academic community.Key topics covered are:Basic principles of academic writingProper citation standards (e.g., APA)Conducting comprehensive literature reviewsStructuring research projects Formulating research questionsDeveloping testable hypothesesEffective use of visuals and data in writing

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design.Students will create a brief research paper to apply the theoretical concepts in practice.

• After successful completion of the course, students willProfessional competenceEffectively communicate complex research topicsSystematically structure a research projectFormulate empirically testable hypotheses - Methodological competenceApply appropriate citation standards to their writing, adhering to the university’s guidelinesConduct detailed and systematic literature reviews, integrating a wide range of sources into their workEffectively use visuals and data to communicate their researchDevelop clear and precise research questions

• Students are provided with lecture slides and supplementary material (e.g., selected journal articles).

Written exam (50%), Research paper (50%); Attendance is mandatory (80%)

Deep Learning and Advanced AI Techniques cover the basics of deep learning and advanced AI techniques and recent technological trends. It also includes a few aspects of generative AI. The course covers:

• Fundamentals of artificial intelligence
• Reinforcement learning – Learning to play games and beyond
• Fundamentals of deep learning, network design, and training
• Transfer learning and pre-trained models
• Data augmentation and synthesis
• Core ideas of: Graph Neural Networks, Autoencoders, Generative adversarial networks (GANs), recurrent neural networks, convolutional neural networks, diffusion models
• Explainability and interpretability in AI
• Case studies and applications in various industries and for various tasks
• Recent trends and future directions in AI and deep learning

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.

After successful completion of the course, students will

Professional competence
• understand the basic concepts and methods of artificial intelligence and deep learning
• be able to identify suitable applications for artificial intelligence and deep learning
• understand key concerns in adopting and leveraging artificial intelligence

Methodological competence
• select, use, and adjust existing models and methods for a given task or data set

Personal competence
• critically reflect on analytical outcomes
• be able to improve and mitigate self-inflicted errors

Technological competence
•be able to use a deep learning framework such as Keras

• Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Harlow, UK: Pearson.
• Bishop, C. M. (2024). Deep Learning Foundations and Concepts. Springer.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: The MIT Press.

Written exam

This optional module allows for various kinds of research studies. It is closely connected to the five units of the Liechtenstein School of Architecture and is usually part of ongoing research projects. The supervision consists of directing the students towards clear results within a given field of research. The individual study is reviewed within the respective unit.

Urban Design & Spatial Development Unit:
What elements make up a neighbourhood that allows people to live without a car? Is it public transport and bike lanes, cafés and bars, shops and pharmacies or parks? How much of each and at what distance? In this independent study, you will measure the vitality of village centres in Liechtenstein, discuss what constitutes a car-free neighbourhood worth living in and how these centres can be improved.
The study is part of the research project 'Vitality analysis in the Rhine Valley - The x-minute city in the context of low to medium settlement density', which is being conducted by Dr Luis Hilti and Prof Michael Wagner from 2023 to 2026.

Self-defined design or research studies, developed individually or in groups agreed upon with research units and under the guidance of mentors. The size of the module is determined by the respective unit.

After successful completion of the course, students will be able to

Relevant reading will be made available at the beginning of the course. A list of recommended literature will be announced in the course and updated on an ongoing basis.

Minimum 75% compulsory attendance, regular meetings with instructors, continuous assessment, portfolio and final review.
The final grade is calculated according to the weighting of the following components: final submission (80%) and oral presentation (20%).

Individual appointments will be set with the tutor.

You can register for this independent study alone or in a group. For registration and enrolment, please get in touch with Luis Hilti directly: luis.hilti@uni.li