Computer Science

The extensive independent study field research paper produced by the student is both the centerpiece of the intern's professional engagement and the culmination of the academic achievements of the semester. During the preparatory session, IFE teaches the methodological guidelines and principles to which students are expected to adhere in the development of their written research. Students work individually with a research advisor from their field. The first task is to identify a topic, following guidelines established by IFE for research topic choice. The subject must be tied in a useful and complementary way to the student-intern's responsibilities, as well as to the core concerns of the host organization. The research question should be designed to draw as much as possible on resources available to the intern via the internship (data, documents, interviews, observations, seminars and the like). Students begin to focus on this project after the first 2-3 weeks on the internship. Each internship agreement signed with an organization makes explicit mention of this program requirement, and this is the culminating element of their semester. Once the topic is identified, students meet individually, as regularly as they wish, with their IFE research advisor to generate a research question from the topic, develop an outline, identify sources and research methods, and discuss drafts submitted by the student. The research advisor also helps students prepare for the oral defense of their work which takes place a month before the end of the program and the due date of the paper. The purpose of this exercise is to help students evaluate their progress and diagnose the weak points in their outline and arguments. Rather than an extraneous burden added to the intern's other duties, the field research project grows out of the internship through a useful and rewarding synergy of internship and research. The Field Study and Internship model results in well-trained student-interns fully engaged in mission-driven internships in their field, while exploring a critical problem guided by an experienced research advisor.

This course uses logic and discrete mathematics to model the science of computing. It provides a grounding in the theories of logic, sets, relations, functions, automata, formal languages, and computability, providing concepts that underpin virtually all the practical tools contributed by the discipline, for automated storage, retrieval, manipulation and communication of data.

This course explores generative artificial intelligence (GAI) and its applications. Students gain a comprehensive understanding of generative models, including deep learning architecture, and probabilistic models. The course covers theoretical foundations and practical implementations of generative AI algorithms. Students also engage in hands-on projects to apply generative AI methods. Topics include introduction to generative AI (overview of generative modeling, brief history of GAI, applications of GAI), probability theory and information theory, parameters estimation, latent variable models, variational inference (introduction), variational autoencoders (VAEs) - autoencoders - variational autoencoders (VAE) - conditional VAE - VQ-VAE v1, v2, generative adversarial networks (GANs) - introduction to GANs - GAN training, issues and solution - generative model evaluation, GAN variants: DCGAN, CGAN, WGAN, ProGAN and Style-GAN, GAN applications: image manipulation and editing, diffusion-based generative models - DDPM - DDIM, diffusion-based generative models - classifier guidance DMs - classifier-free guidance DMs - cascaded DMs - latent DMs, autoregressive generative models - MADE, PixelNN, language generative models - Transformer - GPT family, multi-modal generative models - DALL-E (DALL-E 2 and DALL-E 3) - stable diffusion, flow-based generative models - RealNVP, GLOW.

Prerequisite: Solid understanding of machine learning and deep learning principles - Proficiency in programming - Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

This course examines computation and data handling, integrating sophisticated use of existing productivity software, e.g. spreadsheets, with the development of custom software using the general-purpose Python language. Students will see examples from many domains, and be able to write code to automate the common processes of data science, such as data ingestion, format conversion, cleaning, summarization, creation and application of a predictive model.

This course offers a broad introduction and critical review of recent trends in the field of digital humanities, with particular attention paid to applications relevant for the study of premodern societies (history, archaeology, anthropology, theology, museum studies). The course is divided into four broad themes – text, image, place, and object – highlighting an extensive interdisciplinary range of evidence that both sits within students' fields of study and encourages them to create connections with parallel avenues of scholarship. Following these themes, the course introduces cutting edge tools, successful research projects, and recent scholarship that have leveraged digital advances to fundamentally reshape our understanding of the past. Simultaneously, it engages with more complex topics concerning the ethical and methodological implications of the “Digital Turn” in humanistic studies and its implication for more traditional modes of enquiry. As a whole, this course prepares students to both more substantively engage with digital methodologies and their potential for novel research in religious studies, broadly defined. The course provides hands-on experience developing fundamental skills in digital humanistic scholarship, developing a “Digital Toolbox” that allows students to both undertake digital scholarship in their own studies and to critically engage with ongoing trends and projects relevant to their own research. These tools include, but are not limited to, introductions to GIS, database development, 3D modeling, text encoding, large language models, network modeling, and semantic modeling. Special attention is paid to ongoing research at the University of Copenhagen, highlighting the fundamental skills and research objectives of the diverse research programs taking place throughout the university. The Faculty of Theology, in particular, hosts several compelling case studies for the development and implementation of digital humanities and offers a behind-the-scenes look at these methods in action.

This course is an extension of the Engineering Statistics and Computer Programming courses. The course works extensively with real-world data (relevant to engineering, physics and the environment). The knowledge learned from the aforementioned two courses will be briefly reviewed and further strengthened through a series of hands-on projects. This course enables students to develop solid data analytical skills and problem-solving mindsets, both useful skills for future employment in industry and academia. 

Course Prerequisites: Engineering Statistics and Computer Programming. 

This course surveys basic mathematical tools for deep learning research. The course includes 1) advanced probability theory, 2) information theory, and 3) optimization theory. Topics include introductions to learning theory, review on probability theory, multidimensional Gaussian variables, Gaussian processes, optimal linear estimation, parameter estimation, bias and variance of an estimator, introduction to information theory, entropy, mutual information, KL Divergence, applications of information theory, introduction to optimization, stochastic gradient descent and its convergence, and other optimization techniques and its convergence. 

This course examines relevant OS issues and principles and describes how those principles are put into practice in real operating systems. The contents include internal structure of OS; several ways each major aspect (process scheduling, inter-process communication, memory management, device management, file systems) can be implemented; the performance impact of design choices; case studies of common OS (Linux, MS Windows NT, etc.).

This course covers computer programming for designers. Viewing media in the perspective of physical computing and going beyond the limited functionality of the related applications, students will study the necessary tools and scripting interface to be able to actively use media interaction and control. The course utilizes a scripting language open software program called Processing and its related software like Arduino, iCube and python. Students will complete a project, mid-project workshop, and final project presentation.  Topics include basic geometry, Loop 1, Loop 2, generative drawing, random and noise, generative typography, algorithm drawing, and more.

This is an independent research course with research arranged between the student and faculty member. The specific research topics vary each term and are described on a special project form for each student. A substantial paper is required. The number of units varies with the student’s project, contact hours, and method of assessment, as defined on the student’s special study project form.