Denmark

This course covers structure and mechanisms in organic chemistry with an emphasis on physical organic chemistry. Topics include chemical bonding and structures; stereochemistry; conformational, steric, and stereoelectronic effects; solutions and non-covalent binding forces; acid-base chemistry; energy surfaces and kinetics; isotope effects; linear free energy relationships; catalysis; nucleophilic substitution; addition, elimination, rearrangement, isomerization; concerted pericyclic reactions; radical reactions; and organometallic chemistry.

This course provides an overview of modern vision techniques used in man and machine. It focuses on both conceptual understanding of the models and methods as well as practical experience. The course covers state-of-the-art methods for image analysis including how to solve visual processing tasks such as object recognition and content-based image search and retrieval. It provides the necessary mathematical background to understand vision and image processing methods through programming exercises, which include converting a theoretical algorithmic description into a concrete program implementation, comparing computer vision and image analysis algorithms, and assessing their ability to solve a specific task. The course involves a mix of lectures and exercises.

This course analyzes and discusses core concepts of the philosophy of sociality by focusing on contributions from classical and contemporary phenomenology and philosophy of mind. Topics include empathy, collective intentionality, varieties of groups, varieties of being together, online sociality, and social (in)visibility.

This course considers how colonial legacies and forms of colonial governance persist today. It does so by examining debates within anti-colonial and postcolonial theory about the aftermath of colonialism. In particular, it considers how postcolonial thought articulates conceptions of freedom, justice, the state, and democracy and how these challenge liberal and republican ideas. Moreover, the course considers how postcolonial and anti-colonial thought has influenced the formation of other critical traditions including abolitionist thought, poststructuralism, surveillance studies, and critical border studies. In doing so, critical reflections on colonialism and empire offer new ways to think about state and corporate power, political subjectivity, violence, and borders and migration.

This course explores major themes, patterns, developments, and conflicts in American history, politics, and society, from the pre-colonial era to the present day. Drawing on a range of primary, secondary, and tertiary sources, both historical and contemporary, it outlines phases, continuities, and changes in the nation’s history, identifies key ideologies and institutions, introduces theories and analytical methods that shed light on the nation’s development, and highlights how understandings of the present-day United States call for an informed, critical knowledge of its past. The course includes topics such as liberty and equality, individualism and community, nationalism and regionalism, self-reliance and welfare, business and labor, slavery and race, immigration and identity, ethnicity and gender, domestic reform and overseas expansion, and hot and cold wars. It also addresses the growth of the United States from its origins as a British colonial outpost to its contemporary status as global superpower. In addition, the course enables students to produce written work on topics within its subject areas.

This course provides an introduction to programming in order to numerically solve simple economic models and perform basic data analysis. The first part of the course introduces programming using the general-purpose Python language. It teaches how to write conditional statements, loops, functions, and classes; print results; and produce static and interactive plots. It provides an opportunity to solve simple numerical optimization problems; draw random numbers; run simulations; test, debug, and document code; and use online communities proactively when writing code. The second part of the course instructs how to import data from offline and online sources, structure it, produce central descriptive statistics, and estimate simple statistical models on the data. The third part of the course introduces the concept of a numerical algorithm to write simple searching, sorting, and optimization algorithms, solve linear algebra problems, solve non-linear equations numerically and symbolically, find fixed points, and solve complicated numerical optimization problems relying on function approximation. The course provides hands-on experience with applying the above techniques to solve well-known microeconomic and macroeconomic problems through both a small data analysis project and a larger model analysis project based on a well-known economic model.