Computer Science

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.

This course is an introduction to computability theory and Gödel's incompleteness theorems. The first half of the course focuses on computability theory, and includes Recursive and primitive recursive functions; Turing machines and computable functions; basic results in computability theory including Kleene's Normal Form Theorem, the s-m-n Theorem, Kleene's Recursion Theorem, Recursively enumerable sets, the halting problem and decision problems in general; as well as hierarchy theory, relative computability, and Turing degrees. The second part of the course focuses on Gödel's first incompleteness theorem, and includes Axiom systems for number theory, representable relations and functions, arithmetization of syntax, the Fixed-Point Lemma, and Gödel's first incompleteness theorem, as well as Gödel's second incompleteness theorem.

This course introduces the technologies of computer graphics and human-computer interaction along with the biological, psychological and social aspects of human perception and action that inform the application of those technologies. The emphasis is on 2D and 3D computer graphics and the geometric modelling techniques used for representing and interacting with objects in dynamic scenes. Techniques considered include transformation geometry, illumination models and the real-time rendering (shading) models. The course is centered on developing Apps for tablet computers based on natural user interfaces (NUIs), a term used by developers of human-machine interfaces that effectively become invisible to their users through successive learned interactions. Technologies likely to be considered are: virtual reality, computer games, augmented reality, tele-presence, or other modalities such as interaction through the sense of touch, audio or image processing and analysis.