University of Copenhagen

Lectures and seminars cover reproductive and vegetative development, genome analysis and gene regulation, light and hormone signaling, environmental stress & disease, and applied plant biotechnology. Laboratory exercises cover plant development, anatomy and mutants, transgenics and genotyping, gene cloning, DNA and protein bioinformatics and model organism genome databasing, and protein expression.

This course explores the rich anthropological literature on the multifaceted dimensions of self- and personhood across the Asia-Pacific region to collectively reflect on what personhood entails from an array of non-western vantage points. The Asia-Pacific region is home to a range of distinct indigenous intellectual traditions that have developed and interacted for millennia, each with their own ideas of self and personhood. This diverse cultural context provides fertile ground for examining how spirituality, kinship, modernity and technology intersect with individual and collective identities. By engaging discussions and comparative readings across the region, the course navigates the complexities of identity construction in relation to themes like animism, multiple selves, religion, citizenship and colonialism and examine how variedly communities grant personhood to spirits and monsters, robots and artefacts, animals and nature. 

This course covers various topics on supervised learning (regression, classification) on tabular data, including fundamentals of statistical learning; linear models with and without penalization; course of dimensionality in nonparametric models; additive models; tree based methods and neural networks; and post-hoc interpretability.

The course is a continuation of CS 141A MACHINE LEARNING A course and provides deeper theoretical foundations of machine learning and a number of advanced theoretically grounded learning techniques. A tentative list of topics includes: basics in optimization theory, basics of information theory, advanced techniques for analyzing generalization power of learning algorithms, Kernel methods, ensemble classifiers and weighted majority vote, and Bayesian inference. Prerequisite: CS 141 MACHINE LEARNING A. 

This course offers an advanced introduction into web recommender systems. The goal is to understand and model Web Information and to design and evaluate some of the major technologies operating in the area of web recommender systems through applied projects. Topics include basics of recommender systems (collaborative filtering and content based); evaluation of recommender systems; advanced recommender systems (knowledge-based, ensembled based, hybrid); exploiting additional sources of information for recommendation, e.g., context, location and time.

This course introduces artificial intelligence (AI) applications, with particular attention to the current use of AI systems in the humanities. It reflects on the ethical implications of AI in teaching and learning contexts (e.g. for text production, translation, and language learning) as well as in a series of real-world cases. Contexts and cases focus on English language use, learning and teaching. The course introduces how generative AI systems work, including its reliance on the English language and Anglophone cultures, and the general issues covered in the course. The structure of the course consists of four blocks: bias, hallucinations and transparency; the workings of generative AI systems (LLMs and prompting), as well as data security and privacy; social inclusion and exclusion caused by the application of AI systems; and environmental impacts of using generative AI. Each block introduces students to a series of ethical issues surrounding AI use in the humanities and within the context of the English degree. These examples allow students to analyze the implications of AI in society. Throughout, important ethical issues concerning AI use are presented and critically discussed in class.

This course introduces a number of different texts ranging from the canonical via the postcolonial to the contemporary. The course engages in the affective turn in literary studies and explores the relationship between literature and emotions. In turning to critical theory, it also considers how affective science has impacted on literary studies. The course is organized around thematic clusters, zooming in on emotions such as passion, anger, shame and grief.