Denmark

This course introduces the Danish welfare model and policy, including knowledge of this model's distinctive features in comparison with other models, and the challenges of increased globalization and individualization. Emphasis is placed on the distribution of welfare in relation to income, education, labor market attachment, health, and consumption. The course examines welfare at the individual level, across generations (social and economic mobility), and at the macro level (social cohesion). It focuses on welfare distribution in relation to class, gender, age, and ethnicity, as it deals with the consequences of failure welfare in different parts of the population. The course discusses relations between welfare and labor market models in Denmark and in other countries. It analyzes conditions and living conditions of both the so-called standard population and for vulnerable groups such as the poor, long-term unemployed, and socially marginalized families and individuals. The teaching focuses on understanding how social factors affect communities and individuals' welfare, and how individual circumstances and choices can affect the situation of each person or group is in. The course offers insights into how specific welfare policies affect people's living conditions, and how the "system" meets public/users in various forms of welfare provision. 

This course provides fundamental knowledge of the issues, insights and methods of psycholinguistics. It also trains basic methods for data collection, data processing, statistical analysis, academic argumentation and presentation. The course includes various cognitive models which form a bridge between language as a communicative phenomenon and language as part of human biology. The course provides insight into and experience of using basic tools connected to the planning, implementation, and statistical analysis of a scientific experiment. The course also provides insight into some of the psychological and neurobiological processes which are the foundation of language. Students also acquire skills in quantitative research which can be used in independent work. The course is closely linked to analytical courses in phonetics/phonology, grammar, semantics and pragmatics, and to courses in conversation analysis, language development, and sociolinguistics.

This course explores key philosophical questions about consciousness as it relates to the world. It discusses the connection between intentionality and phenomenal consciousness, the relationship between object consciousness and self-consciousness, and the link between consciousness and the self. On the basis of central texts in contemporary theoretical and practical philosophy, as well as more historical texts in classical phenomenology, the course addresses questions of personal identity: how the self pertains to personal identity, the nature of personal identity over time, how persons persist despite undergoing physical and psychological changes, and the prudential and moral significance of personal identity. The course involves lectures, oral exercises, and group discussions.

This course provides an introduction to the field of quantum computing and information, covering a variety of topics ranging from computation and cryptography to foundations of quantum physics. It explores current research topics and discusses how quantum phenomena give rise to new algorithms for machine learning, quantum computational supremacy, cryptographic schemes with unprecedented security guarantees, and device-independent protocols. Topics include fundamentals of quantum computing; the circuit model; basic quantum algorithms and the concept of quantum computational supremacy; Bell inequalities, non-local games, and the concept of device-independence; and basic quantum protocols for cryptography. As part of the exercises, students run simple quantum programs on an actual quantum computer available through the cloud.

This course covers online and reinforcement learning, concepts that break out of the static realm and move into the perpetual cycle of receiving new information, analyzing it, and executing actions based on the updated estimation of reality. This course considers the agents (computer programs, robots, living beings) that learn based on interactions with real or simulated environments: repeated investment in the stock market, spam filtering, online advertising, online routing, medical treatments, games, and robotics. The course also situates online and reinforcement learning to model a much richer range of problems, such as limited and delayed feedback; and even adversarial problems, where the environment deliberately acts against the algorithm (chess, spam filtering). Mathematical tools for developing and analyzing algorithms for these problems are also studied.

This course provides an advanced approach to theoretical and practical genomics with a focus on mammals. The course introduces methods and technologies currently used to dissect, describe, and characterize complex genomes. Aspects of both research and application within the field of genomics is addressed. Topics include animal models and comparative genomics; organization and content of the mammalian genome; human genetic variation; genetic mapping of mendelian characters; mapping genes conferring susceptibility to complex diseases; gene expression and epigenetics; sequencing genomes: techniques, challenges, and bioinformatic analysis; molecular pathology, cancer, and pharmacogenetics; genetic testing of individuals; genetic manipulation of cells and animals, gene therapy, and stem cells; and biomarkers.