I-Week 2019

Course in InSIS – 4SA630, classroom – NB457

François Kaisin is PhD in Biology and has been Director EHS and HR in two international industry groups. He is a professional certified business coach and founder of the Morena Coaching International network in 20 countries. He also teaches about “Project Team Management” at the Business School of the University of Nice. François will be pleased to share the fruit of his reflections on the levers of successful management of project teams, relying on concrete examples of field as a facilitator and coach of project teams going from the Andes to the Swiss Alps, through the Caribbean and the Pampa. He is also coaching trainer in a Canadian School, co-author in a book on employee coaching, emotions and business performance.

Most of the efforts of project teams depend in particular on the human factor, regardless of their scope and complexity. However, there is often a tendency to entrust projects to managers, especially for their technical skills. While today’s access to information is becoming easier and easier, the real challenge lies more in the complexity of human relations, the challenge of young millennials and the business of tomorrow.

Course syllabus:

• Designing and Building an effective Project Team
• Managing Human Resources and Conflicts
• Managing Relationship with Stakeholders
• Planning Strategic Project Communications
• Managing “tomorrow” Project Teams
• Business Cases for decision-makers

Course in InSIS – 4IT470 , classroom – NB470

Erika Kriechbaumer is co-founder and owner of TheMove, an international consultant firm that specials on leadership and technology in organizations 9.0, based in Austria. Previous to that, she hold several senior management positions in a global technology group, recently sales director for North/Latinamerica and Asia Pacific as well as Managing Director in China.

The course opens the mind of the participants for the current changes in consciousness and the impact on society and companies and hence on the leadership competences in IT & technology related organizations. The participants also understand the logic & energy of organization 9.0 and co-create solutions on how to lead smoothly IT projects in organizations 9.0.

Course syllabus:

• Understand the evolution of human consciousness and impact on the individual, society and organizations
• Learn about the new form of organization: organization 9.0
• Create an understanding for the difference between management and leadership 9.0
• Understand how fundamentally the new leadership competences change the current logic from „cause-and-effect“ to a new understanding that an organization is a co-creation of reality.
• Work out together the implications on leadership in IT & technology related topics and organizations and how this can be done.

Course in InSIS – 4SA434, classroom – NB459

Alex E. Kossovsky is the author of the books “Small is Beautiful: Why the Small is Numerous but the Big is Rare in the World” and “Benford’s Law: Theory, the General Law of Relative Quantities, and Forensic Fraud Detection Applications”. He is the inventor of a patented statistical algorithm in data fraud detection analysis, registered at the US Patent Office. He specialized in Applied Mathematics and Statistics at the City University of New York, and in Physics and Pure Mathematics at the State University of New York at Stony Brook.

The purpose of this course is to enable statisticians to judge whether provided data is fraudulent and imaginarily concocted, or truly authentic and honest. The methods of this course focus on the way our ten digits are spread within numbers of random data. Surprisingly, the distribution of the digits is not random at all but rather highly predictable and follows consistent patterns. Fake data sets do not have these digital patterns and therefore are easily identified as suspicious and possibly fraudulent.

 

Course syllabus:

• Benford’s Law: Common intuition tells us that all digits within numbers in real-life data should occur randomly with equal chances. However, empirical examinations contradict this and consistently show that not all digits are created equal. The theoretical prediction of the proportions of digits within numbers of real-life data is known as Benford’s Law.
• Digit Distribution in data: Low digits such as {1, 2, 3} occur much more frequently than high digits such as {7, 8, 9} in the first place of numbers on the left-most side.
• Example I: The proportion of numbers in data that start on the left-most side with digits 1 or 2 is about 48%, hence numbers such as 145, 10936, 28, 145, 2007, 21, are relatively frequent.
• Example II: The proportion of numbers in data that start on the left-most side with digits 8 or 9 is only about 10%, hence numbers such as 976, 805, 9032, 825, 93, 872, are relatively rare.
• Applicability of the Law: This digital phenomenon occurs in almost all data types, such as those relating to geology, chemistry, physics, biology, engineering, economics, accounting, finance, econometrics, demographics, and election vote data sets.
• Data Forensics Application: Benford’s Law is immensely useful as a tool to detect tax fraud, financial fraud, as well as election vote fraud – via the examination of the digital configuration of a given data set.

Course in InSIS – 4IZ570 , classroom – NB458

Eneldo Loza Mencía is a senior researcher at the Knowledge Engineering Group, Technische Universität Darmstadt, one of the leading universities in artificial intelligence related research in Europe. He holds a Ph.D. in computer science on efficient multi-label classification, one of his research focuses. Other research interests include human-interpretable machine learning models, forecasting of epidemiological outbreaks, automatic text summarization, and computer poker AI. In his teaching activities, he lays particular emphasis on assuring a proximity to practice and close cooperation between students. For instance, he teaches the internship courses “Creating Business Value through Data Science” in collaboration with a well-known international consulting firm and “Automatic Text Summarization”.

With the development of information technology, ever larger amounts of data are available,  often containing implicit knowledge that, if known, would have great economic or scientific significance. Data Mining is an area of research that deals with the search for potentially useful knowledge in large amounts of data, and machine learning is one of the  key technologies within this area.
The course provides an introduction to the field of data mining and machine learning with a special focus on base techniques and algorithms used within the field. Students will learn the main characteristics, advantages and disadvantages of the different methods and in which (data) situations and under which circumstances to employ them by investigating their internals.

Course syllabus:

• Concepts – problem settings in data mining
• Classification Methods
  • instance-based approaches
  • decision tree learning
  • neural networks
  •  ensemble techniques
  •  evaluation
  •  pre-processing
  •  semi-supervised and unsupervised methods
• Further Topics
  •  text mining
  •  information retrieval
  •  recommender systems
  •  reinforcement learning

Course in InSIS – 4EK613  , classroom – NB456

Dr. Petr Mariel is an Associate Professor at the Department of Econometrics and Statistics of the University of the Basque Country UPV/EHU (Bilbao, Spain). He has a Master
in Economics and a Ph.D. in Economics from the University of the Basque Country UPV/EHU. He teaches basic and advanced quantitative methods master courses to Economics students.
His research focus is centered on discrete choice modelling applied mainly to environmental valuation, but he has also worked in the field of health, public and urban economics. He is the co-founder of a European scientific network of researchers using discrete choice modelling in the field of environmental valuation (www.envecho.com) and member of the editorial advisory board
of the Journal of Choice Modelling.

The purpose of this course is to introduce students to Discrete Choice Experiments, a methodology devoted to analyse people’s preferences. This methodology involves the generation and analysis of choice data through the construction of a hypothetical market using a survey. The course covers full process of undertaking a choice experiment, including survey and experimental design, econometric analysis of choice data and welfare analysis.

Course syllabus:

• Introduction to Discrete Choice Experiments (DCE)
• Examples of DCE applications
  • Job preferences of business and economics students in Czech Republic, Germany and Spain
  • Electricity services in Kenya
  • School choice in the Basque Country
  • How much does a mountain cost? Economic valuation of the Mount Jaizkibel
  • Wine production and climate change in Spain
  • Preferences of German citizens toward wind turbine
  • Willingness to pay for health services in the Basque Country
  • Valuing preservation of threatened lynx populations in Poland
• Designing a DCE
  •  Questionnaire development
  •  Experimental design
  •  Survey administration
• Econometric Modelling
  • Estimators and estimations
  • Models for discrete choice analysis
  • Introduction to R
  • Discrete choice model estimation in R
  • Interpretation and welfare measures
• Frontiers in DCE