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This course consists of practical project work or audit and an evaluation field stay including technologies and background information necessary to develop sustainable community-based projects, e.g. PV training, CO2compensation, household biogas plants, clean cooking, biogas, income generation. International student hybrid working groups develop CO2 compensation projects for climate and SDGs tackling the needs of the local partner communities together with the partner NGOs. The course offers research and innovation opportunities to deepen the development and application of sustainable technologies and methodologies. It also includes cooperation with local community organizations, NGOs, international universities, and other partners.

This course's aim is to develop an understanding of large language models (LLM) as well as their evaluation and to practice reading, understanding, and presenting research work. As part of the class, methods for the selection of data and LLMs, data preparation, application and evaluation of LLMs are developed and put into practice. The use cases are examples from the field of natural language processing, e.g. translation, summary of text, or information extraction.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor.

Climate change is no longer an abstract future threat. Human population is at the center of the climate system. A demographic perspective is hence critical for understanding, on the one hand, the impact of human activities on the global climate, and, on the other hand, the impacts of climate change on human population. Upon successful completing of this course, students have the knowledge and skills to: 1) demonstrate an understanding of how human population contributes to anthropogenic climate change taking into account demographic heterogeneity; 2) demonstrate an understanding of how anthropogenic climate change differentially affects human health, wellbeing and livelihoods; 3) critically evaluate and explain different scientific and statistical evidence employed to study the links between population dynamics and climate change; 4) conduct research through the consultation of academic literature and/or through the collection and analysis of data; 5) work in groups and develop class discussions. The course topics include:

• Introduction to population and climate change interactions
• Climate change and demographic heterogeneity (e.g. age, gender, education, income, locations)
• Population and energy consumption/carbon emissions
• Population, water, and food
• Climate change and health and mortality
• Climate change and family and fertility
• Climate change and migration
• Climate change and future population dynamics
• Date and methods for the study of population and climate change

This course introduces the study of democracy and democratization, which is one of the core sub-fields in comparative politics. Drawing on classic and cutting-edge work, it covers four major topics in the field: the concepts and typologies of democracy, regime transitions from authoritarianism to democracy, the consolidation of democracy, and democratic backsliding. The course helps students develop theoretical understanding about what democracy is and how democracy can be established and developed, or de-consolidated. 

The course includes designated class discussions/debates on South Korean democracy and democratization. Therefore, a substantial level of knowledge of Korean politics is needed to participate in class discussions/debates. 

This course extends the statistical ideas introduced in the first year to more complex settings. Mathematically, the central concept is the linear model, a framework for statistical modelling that accommodates multiple predictor variables, continuous and categorial, in a unified way. There is a focus on fitting models to real data from a variety of problem domains, using R to perform computations. 

This course covers programming using data programming language on an introductory yet rigorous basis for highly motivated students with little or no prior experience in programming. The course focuses on collecting and analyzing data, as well as the grammar of the data programming language and provides an in-depth look at big data analysis.  

Modern scientific, engineering, and business applications are increasingly dependent on data, yet traditional data analysis technologies were not designed for the complexity of big data. Big data analysis has emerged as a new, exciting, and fast-paced discipline that explores novel statistical and implementation challenges that emerge in collecting, processing, storing, and extracting knowledge from big data.  

Students learn how to collect, process, and analyze large amounts of data by combining data analysis technology and artificial intelligence technology.  Students will use Python as a powerful tool for data management and analysis and analyze structured and unstructured data using LLMs for natural language processing, text analysis, and graph-based multi-agent systems.  In addition, data management and system design techniques using RAG (Retrieval-Augmented Page 1 of 9 Generation) and APIs that support prompt engineering techniques and AI-based decision-making will also be covered. 

This is a useful course for students who want to combine and utilize data analysis and artificial intelligence technology in various fields such as information technology (IT), business analytics, marketing, and strategic planning. 

Prerequisites:  Basic knowledge of the system configuration of the operating system (Windows, macOS, etc.) (e.g., setting environment variables, etc.).  Basic knowledge of the Python programming language 

This advanced topics course covers reinforcement learning, search, and test-time scaling of large language models that are expected to drive the next generation of AI systems. 

Topics include: Basics of RL (Markov Decision Process and Policy evaluation), Basics RL (Imitation learning, Deep policy gradient methods), Basics of RL (Deep Q-Learning, Rainbow DQN); Symmetric alternating Markov games, Monte Carlo tree search, expert iteration, and AlphaGo; Imperfect information games, Counerfactural regret minimization, and Pluribus; NLP basics (RNN, beam search, tokenizers); NLP basics (Transformers, encoder-decoder architectures); Instruction fine-tuning, Scaling laws of LLM pre-training; Reinforcement learning with human feedback, direct policy optimization, Group Relative Policy Optimization (GRPO); Chain of thought, Process reward models, Prover-verifier games; In-context learning, Scaling LLM Test-Time Compute; DeepSeek-R1. 

This course introduces and elaborates on diplomatic practices, such as negotiation and mediation. Central issues discussed and compared are varieties of diplomacy, newness and decline of diplomacy, cultural differences in international negotiation, and third parties in international negotiations. Practice and strategies are linked to various diplomatic actors, such as states, intergovernmental organizations (IGOs), nongovernmental organizations (NGOs) official, and unofficial diplomats. The course stresses the linkage between theory and practice in international relations.

This course discusses differential geometry of curves and surfaces in Euclidian Space: curves in 2- and 3-dimensional spaces, local and global theory of surfaces, special classes of surfaces, discrete curves and surfaces.

This course will help students to 1. Understand the origin, development, clinical application and dominant diseases of acupuncture and moxibustion; 2. Be familiar with the experimental progress and scientific research of acupuncture and moxibustion in the treatment of pain, mental disorders, reproductive metabolic diseases, degenerative diseases, bone and joint diseases, etc.