• This course is designed to foster critical thinking skills among first-semester Master in Finance students. The mod-ule consists of two input sessions, where foundational concepts and techniques of critical thinking are introduced, followed by four discussion sessions. In these discussion sessions, students will analyse and debate current topics with a strong emphasis on applying critical thinking principles to evaluate arguments and evidence.Key topics covered are:Introduction to critical thinkingAnalytical techniquesLogical reasoningBias identificationArgument evaluationCurrent topic discussionsInteractive debatesEvidence-based analysisReflective thinking

• Input Sessions:Socratic Method: Use guided questions to introduce foundational concepts and techniques of critical thinking. Encourage students to think deeply and articulate their thoughts.Mini Lectures: Brief lectures to provide essential background information and context for the discussion topics. - Discussion Sessions:Structured Debates: Organize students into groups to debate current topics. Each group prepares argu-ments for and against the topic, ensuring they apply critical thinking principles.Role-Playing: Assign students different perspectives or stakeholder roles to argue from, enhancing their ability to understand and analyse diverse viewpoints. - Case Studies:Real-World Analysis: Provide case studies related to current financial issues. Students analyse and discuss these cases, applying critical thinking to evaluate different outcomes and solutions.

• After successful completion of the course, students willProfessional competenceUnderstand critical thinking principles.Apply critical thinking to financial topics.Analyse complex financial arguments. - Methodological competenceCreate logical frameworks.Evaluate evidence and sources.Apply analytical techniques to discussions. - Social competenceUnderstand diverse perspectives.Communicate effectively in debates.Analyse and respect differing viewpoints. - Personal competenceDevelop independent thinking skills.Improve reflective thinking abilities.Evaluate personal biases. - Technological competenceUtilize online platforms for discussion and collaboration.

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

Written exam (40 %), Discussion participation (30 %), Reflection paper (30%)

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.

Sustainable Design Unit:
In this module, you will engage in a detailed analysis of a sustainability indicator such as Environmental, Social, and Governance (ESG) criteria, Sustainable Development Goals (SDGs), or building standards like LEED or DGNB. You will explore how these indicators can be integrated into a sustainability matrix. The scope and complexity of your research, as well as its relevance to architectural inquiries, will determine the workload, which will be agreed upon in consultation with your supervisor. Your interests and prior knowledge will be taken into consideration at the outset of the project. This elective module provides an opportunity to engage in research focused on defining sustainable building practices within the Alpine Rhine Valley. In alignment with ongoing research at the LSA, the module is centered on the development of a comprehensive catalogue of themes related to sustainable construction. Through comparative analyses of building labels and broader sustainability frameworks, you will examine how these themes can guide sustainable architectural practices in the region. This module presents a unique opportunity to contribute to the academic discourse on sustainability and to shape future building practices. Participants will conduct self-directed research working either individually or in collaborative groups. The scope of your work will be customized to align with the specific requirements of your project and will be overseen by the research unit. Essential literature and resources will be provided at the start of the course, with continual updates throughout the semester to support your research on sustainability in architecture.

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.

Group projects are also possible, as well as group work with individual submissions.

The course is an introduction to the field of Finance, reiterating the most important concepts from a bachelor's degree with a focus on Finance. It builds on the time value of money principle and applies it to the valuation of bonds, interest rates, and capital budgeting. The course also highlights some of the most significant markets for financial instruments. The main goal is to establish a strong foundation for understanding the key concepts of Finance.
Key topics covered are:

• Introduction to financial markets
• Interest rates and bond prices
• Structure of interest rates
• Market efficiency
• Funds markets
• Money markets
• Bond markets

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical de-sign and analysis skills.

After successful completion of the course, students will

Professional competence

• Understand foundational finance concepts.
• Apply the time value of money principle.
• Evaluate bond pricing and interest rates.
• Analyse the role of funds.

Methodological competence

• Analyse the structure of interest rates.
• Evaluate market efficiency.
• Apply valuation techniques in capital budgeting.

Social competence

• Discuss financial market concepts with peers.
• Communicate complex financial ideas effectively.

Personal competence

• Develop a deeper understanding of financial markets.
• Improve critical thinking related to finance.

• Berk, DeMarzo: Corporate Finance, Global Edition, Fifth Edition.

Final Written Exam

The Pro Bono Project emphasises the importance of social responsibility and offers students a platform to engage in nonprofit activities for the well-being of society. It provides an opportunity to initiate, develop and implement a project in collaboration with partners from practice (e.g., municipality, association, school, NGO, etc.). The project is studentled, with the guidance of mentors. It can be either a built intervention or an activity, but it must have a connection to the built environment and serve the common good. The Pro Bono Project is intended to bring added value both to the Liechtenstein region and to the international context. Collaboration between Bachelor´s and Master´s students, as well as with students from other programs at the University of Liechtenstein is encouraged. Both group projects of up to five students and individual projects are possible. The Pro Bono Project has to be linked to at least one unit of the Liechtenstein School of Architecture.

Student-led project work under the guidance of mentors. Sustainable action within a self-selected environment is critically analysed and applied. The Pro Bono project allows students to establish a close link to practice. By preparing a Pro Bono Project, students gain an in-depth insight into the three main areas of professional activity: concept phase, implementation phase and reflection phase. Students can complement and test their theoretical studies with practice-relevant
work.

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

Students are provided with supplementary material in accordance with their individual projects (e.g., selected handbooks for participatory design methods, best practice examples, focus area related literature).

Minimum 75% compulsory attendance
The introduction, two inputs as well as four mentoring meetings – the first two during the planning phase (before submitting the application) and the other two during the implementation phase – are compulsory.
The final grade is calculated from the weighting of the following components: project application (20%), final report (40%) and project presentation (40%).

For Bachelor students: submissions can be done in German

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

• The course involves interactive seminars with workshops and regular presentations.
• The faculty and a jury of representatives from regional companies evaluate the students’ solutions in terms of innovativeness and usefulness and provide them with feedback and advice.

After successful completion of the course, students will

Professional competence

• be able to analyse real-world cases
• integrate knowledge to identify areas of improvement or innovation
• use appropriate methods to develop recommendations for a case company

Methodological competence

• manage a (small) project
• identify and structure existing information
• work with domain experts (external partners)

Social competence

• self-organise within a group
• work in a group and with external partners
• handling criticism and demonstrate the ability to criticise in a constructive manner

Personal competence

• reflect on limitations of their own work
• work on tasks independently within a group
• manage time

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

Seminar paper (50%), presentations (50%); attendance is mandatory (80%)

Dieses Modul fördert kommunikative und kulturelle Kompetenzen in der deutschen Sprache auf Niveau A2 (gemäss CEFR).

Es handelt sich um voraussichtliche Termine, die nachträglich auf Wunsch der TeilnehmerInnen geändert werden können.

Informationen zum Einstufungstest werden auf der Webseite der fakultätsübergreifenden Wahlfächer > Sprachkurse sowie durch das International Office bereitgestellt.

Es kann nach erfolgreicher Teilnahme die Ausstellung eines Sprachzertifikats (der Uni Lie) angeboten werden. (basierend auf den Ergebnissen des standardisierten Spracheinstufungstests onSET.

Interaktiver Unterricht

Erwerb Sprachkompetenz in Deutsch auf Niveau A2, Erwerb kultureller Kompetenz

• einfache Texte und Dialoge hören, verstehen und darauf reagieren können
• einfache Texte und Dialoge lesen und verstehen können
• Grundgrammatik beherrschen und anwenden können
• Grundwortschatz kennen und anwenden können
• einfache Texte schreiben können
• geeignete Lernstrategien einsetzen
• kulturelle Kompetenz entwickeln

Verfügbar auf Moodle

Prüfungsmodus:
Teilleistung A: Laufende schriftliche und mündliche Leistungsbeurteilung während des Semesters
Teilleistung B: mündliche Prüfung

Anwesenheitspflicht: minimum 80% verpflichtend

Benotung

Es kann nach erfolgreicher Teilnahme die Ausstellung eines Sprachzertifikats (der Uni Lie) angeboten werden. (basierend auf den Ergebnissen des standardisierten Spracheinstufungstests onSET.

Fakultätsübergreifendes Wahlfach:
Regeln für die Anmeldung: www.uni.li/cross-faculty

Advanced Machine Learning covers several advanced topics in the field of machine learning and is concerned with requirements engineering in particular. Students learn to analyse certain types and large amounts of data. The course has three parts: Managing and Conducting Data-driven Projects, Machine Learning (ML) Techniques and Applications, and Large Scale Machine Learning. In more detail, the topics covered are:

• Requirements engineering for machine learning and business intelligence projects
• ML in Production - from models to products that are monitored and updated
• Advanced ML topics, e.g., AutoML, Time series analysis
• Distributed and parallel computing for machine learning and data processing with a focus on Spark

• 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

• have deepened their understanding of the field of machine learning and acquired a large set of machine-learning techniques
• understand the challenges and solutions of processing large amounts of data
• gather requirements for projects in the field of machine learning

Methodological competence

• be able to apply a diverse set of methods to address a number of machine learning problems

Personal competence

• critically reflect on analytical outcomes
• improve and mitigate self-inflicted errors

Technological competence

• use Python libraries for automated machine learning such as hyperopt, time series analysis
• understand large scale data processing frameworks such as Spark

• Burkov, A. (2020). Machine Learning Engineering. True Positive Inc.Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, The Netherlands: Elsevier.

Written exam (60min)

The Professional Practice gives students the opportunity to work in an architectural office or related fields to gain new practical experience in different stages from design studies, competition entries and construction projects up to the on-site building process. The Professional Practice is planned and set up by the students, under the guidance of mentors. Thereby students learn to apply for a new job. They prepare a professional portfolio and document their learnings during the professional practice. Those who already work in an architecture office or in related fields can also complete the Professional Practice module but must arrange a work situation that allows them to gain experience in a new field within their employment for the required duration. The experiences and insights gained during the Professional Practice are presented at the University of Liechtenstein and reviewed in front of the mentors.

Professional practice under the guidance of mentors.
Professional Practice enables students to establish a close link to practice. By working on studies, competitions or real
projects, students gain a deeper insight into various areas and phases of architecture and related fields. By collaborating
on real projects, students are enabled to supplement their theoretical studies with practical work.

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 bas

Minimum 75% compulsory attendance
Two mentoring meetings, the first before starting the professional practice and the second during the professional practice.
The final grade is calculated according to the weighting of the following components: professional practice documentation (60%) and presentation (40%).

The Master's Thesis is carried out by the student as part of an Advanced Studio and is supervised by the unit. The thesis contains a developed hypothesis that is verified (or falsified) as part of the design project. In addition to the actual design project, the Master's thesis includes the design and production of the thesis book. This consists of
three parts: the documentation of the design project, a theoretical report and a technical report. The formal aspects of the thesis and the thesis book are described in the Master's thesis regulations.

Mentored project work in the design studio.

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.

Successful completion of 3 Advanced Studios in the Master´s degree programme.

Minimum 75% compulsory attendance, continuous assessment, midterm and final reviews.
The final grade is calculated according to the weighting of the following components: design project (60%), final presentation (10%) and thesis book (30%: 10% theoretical report, 10% technical report and 10% project documentation).

• Data Management covers the modern data-management cycle, from the collection of data from diverse sources to the preparation of data for data-driven applications. Students learn how to handle various data formats, how to assess and improve data quality, and how to store and process data using SQL, NoSQL, and Hadoop technologies. The course covers eight primary topics:Modern data-management requirementsDatabase system architectureDiagnosing and handling data quality problemsRelational databases (SQL)Hands-on labs with MySQLConcurrency control techniquesNoSQL databases (e.g., MongoDB)Apache Hadoop (HDFS, MapReduce)

• The module 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 of modern data management
• have gained insights in advanced concepts of modern data management

Methodological competence

• be able to apply the data management stack from conceptual database design to data query and manipulation
• be able to collect and prepare data for data-driven applications

Social competence

• be able to organise learning materials and work in groups
• be able to divide problems into meaningfully tasks, work on them and help each other within the group

Personal competence

• be able to apply the learned theoretical content to real-world scenarios
• have an increased capability of structural thinking

Technological competence

• be familiar with SQL, in particular with MariaDB/MySQL
• be familiar with MongoDB

• Elmasri, R., & Navathe, S.B. (2016). Fundamentals of Database Systems, 7th edition. New York: Pearson Education
• Harrison, G. (2015). Next Generation Databases – NoSQL, NewSQL, and Big Data. California: Apress Media

Written exam (60min)