In this course, we teach both advanced machine learning (ML) approaches and hands-on skills for applying these approaches to data science problems.

This course will introduce classes of tasks that are at the core of most real-world production systems. It will teach advanced solutions to solve these tasks on complex and large-scale data with state-of-the-art tools.

This course provides an overview on the topics of bias and fairness in data, models, and algorithms, and on the associated ethical and accountability issues.

In this course, you will learn how to analyze and improve the performance of a computer system with a specific focus on data-intensive systems.

To investigate a system’s performance and come up with solutions to improve it, there are three key things to know.

1. How to devise a methodology to design experiments so that you can investigate the system’s performance,
2. The toolset to run the necessary experiments and collect the results, and
3. At least a basic understanding of the systems layers (hardware, operating systems, etc.) to be able to interpret your results and come up with solutions.

This is what we will cover in this course.

In this course, students learn how to work in data-driven design and development processes and what changes data analysis brings to the design and maintenance of games.

The course will introduce and cover basic concepts in Enterprise Architecture -an approach to understand, manage and develop business strategy in a complex IT landscape. There will be an introduction to the recognised frameworks and models in Enterprise Architecture.

This course aims to enable students to design and develop websites and interactive Web applications based on standard Web technologies and software frameworks.

The subject of the course is functional, declarative programming in general and F# in particular.

This course gives an introduction to IoT as well as practical experience with the design and implementation of end-to-end IoT systems.

Practical examples and exercises will predominatly be from domains Energy and Environment.

The students will learn theoretical foundations of IT-enabled process improvement and apply these to a real life practical context by working with an organisation.

Turning the unprecedented amounts of data being collected today into useful information is well beyond the computing power of a single general purpose CPU core. It is, therefore, crucial to know and understand the methods and tools that are able to parallelize various data analysis tasks in an efficient way on multicore CPUs and on a cluster of machines.

With this goal in mind, this course first gives an overview of the popular parallel data processing platforms. Then, it dives into parallelizing various machine learning tasks.

The objective of the course is to introduce students to important philosophical and historical perspectives on science and technology, as well as to more general epistemological and reflexive issues related to business informatics as a social science discipline. Throughout the course, the student will become acquainted with a series of concepts within a selection of thematic fields that can be used to critically interrogate empirical phenomena relating to ‘science’ and ‘technology’. 

The main outcome of the course is that students are equipped with an understanding of core theories, concepts and methods in co-design and participatory design approaches, and their historical grounding and development. Students will also have experience from applying co-design methods in a live project with real stakeholders, and reflecting on the process and results.

The course runs for the last time in the current format in Spring 2025

The course will be offered in English in Spring 2025 and a slight revision of course is expected.

Kursets mål er, at de studerende har (1) en grundlæggende forståelse af begreberne data, netværk og dataanalyse; og (2) en værktøjskasse med teknikker og teorier til at diskutere ideen om netværk og data som grundlag for konceptualisering af et netværkssamfund.

The course is offered for the last time in the current format in Spring 2025 ref. the revision of the BDDIT programme

This course gives a fundamental overview of Android programming concepts and the best practices for mobile app development.

The course "DevOps, Software Evolution and Software Maintenance" is a BSc elective.

In this course, the students will discover all the software engineering activities that take place after an initial software product is delivered or after a legacy system is taken over from a theoretical and practical perspective. Students (in groups) will take over such a system that is live and serving users, they will refactor and migrate it to the languages and technologies of their liking. All subsequent DevOps, software evolution and software maintenance activities will be performed directly on the systems of the students.

The course "DevOps, Software Evolution and Software Maintenance" is an MSc elective.

In this course, the students will discover all the software engineering activities that take place after an initial software product is delivered or after a legacy system is taken over from a theoretical and practical perspective. Students (in groups) will take over such a system that is live and serving users, they will refactor and migrate it to the languages and technologies of their liking. All subsequent DevOps, software evolution and software maintenance activities will be performed directly on the systems of the students.

The course aims to train the students in conducting a thorough and valid analysis of online data sources with the use of basic programming, statistics and business intelligence tools. 

This course is designed to give students a basic introduction to programming and data processing. Students will get a hands-on introduction to the Python programming language and how to work with tabular data. This will enable them to solve simple programmatic tasks as well as to get an idea of what the role of the programmer entails. After the course student will be able to discuss and reflect on challenges and opportunities derived from approaching the world through algorithms.

The purpose of this course is to explore play as an expressive form and creative framework outside of the domain of games. As such, the learning goals and activities are not oriented towards game design. This course will focus on drawing into the digital design domain inspiration and practices from play and computer games.

This course is designed to expand the intellectual and professional scope of interaction design students.

In collaboration with industrial partners, students will apply Scrum in practice and reason about its application in a controlled yet realistic environment.

Algorithmic problem solving is the art of designing algorithms to solve challenging programming problems. This requires extensive practice in classifying problems, choosing the right data structures, and identifying suitable algorithms. This is very different to a standard algorithm and data structure course, where the focus is on understanding the tools at hand, but not on using them to solve problems. This course bridges the gap between theoretical knowledge and practical solutions to computational problems.

Algorithmic problem solving is the art of designing algorithms to solve challenging programming problems. This requires extensive practice in classifying problems, choosing the right data structures, and identifying suitable algorithms. This is very different to a standard algorithm and data structure course, where the focus is on understanding the tools at hand, but not on using them to solve problems. This course bridges the gap between theoretical knowledge and practical solutions to computational problems.

Through this course, the students will learn about the aspects of game programming commonly involving artificial intelligence methods, which methods are used and how to implement them.

The course provides a basic introduction to scientific thought and introduces central philosophical perspectives on science, epistemology and technology, including central concepts in scientific methodology. The course puts a special focus on IT technologies, exploring its foundations and ethical implications.  

This course provides an introduction to the fundamental concepts of cryptography, the practice of protecting information and securing communication in computer systems. We start with classical encryption techniques, and consider modern private-key and public-key crypto systems and study their security against eavesdroppers or attackers, who tamper with network traffic. The course is aimed at students with a background in computer science or mathematics, and will include both theoretical and programming exercises. 

The course gives an in-depth introduction to the fundamental principles of statistics.

The course offers a comprehensive exploration of service design and management, equipping students with essential processes, fundamental principles, methods, and tools. The course centers on the strategic design and management of services, incorporating digital components and applying expertise from various service design and management disciplines. Through lectures, in-depth case studies, and interactive hands-on exercises, students explore the characteristics and dynamics of 'service' and 'service ecosystems.' This course enhances students understanding of how services can be designed, efficiently managed, and thoughtfully implemented to deliver substantial value to users, service providers, and other stakeholders.

This course examines organizations in light of contemporary challenges with digitalization. It explores organizational perspectives on digitalization and cover theories that can help us better understand, and critically reflect upon, such profound challenges, while also offering hands-on approaches to managing them. Furthermore, it investigates questions of the changing nature of work, and themes such as leadership, management, and organizing in order to make students both knowledgeable about, and skilled in the practice of, and engaging with, digital transformation.

This course provides an introduction to Information Security Risk Management. The course enables students to lead risk assessments, and reflect on the applicability of different techniques for risk assessment and management.

This course provides an introduction to Information Security Risk Management. The course enables students to lead risk assessments, and reflect on the applicability of different techniques for risk assessment and management.

This course is a new course for the KDDIT 2024 intake. KDDIT students enrolled before 2024 and who has not yet taken Design-driven Innovation course, should sign up for KADDINN1KU Designdrevet innovation (see also transitional provisions)  

In this course, students will learn about qualitative research methods and the constitutive elements of qualitative research designs.  

Learn to make custom data visualisations by designing with data. Uncover the possibilities of data visualization and discover how to play with data in code.

The goal of this course is to equip students to understand how underlying technological and social infrastructures shape the digital economy.

This course introduces basics of Bayesian statistics, Bayesian data analysis, Bayesian learning, and the programming tools that enable automation of these methods. The course emphasizes programmable statistical methods over pen and pencil analytics.

The goal of the course is two-fold. On the one side you will learn how to clean, manipulate, process and visualize data in Python with a specific focus on unstrucured data that is typically produced online. On the other side, you will learn how to formulate hypothesys based on this data that can be used in the context of the evaluation or the re-design of a digital product.

This is a course in mathematics covering linear algebra and analysis (calculus) of functions of several variables. These are perhaps the two areas of mathematics that have found most uses in practical applications. In particular, the course equips the student with mathematical tools necessary for analysis of big data.

This hands-on course teaches you how the design, implementation, and implications, of systems software influence your task as an application programmer. You will get an in-depth understanding of how hardware and the operating system work; in the process, you will learn how computers actually work, and what actually happens when you run an application. This enables you to fully exploit underlying hardware and systems software to write high-quality software.

Discrete Mathematics covers different topics in mathematics, which support many disciplines in software development. The goal of this course is to give the students the ability to apply formal reasoning. The first part of the course is dedicated to learning how to construct logical proofs, proofs on set theory and proofs by induction, while the second half of the course builds upon the first part to cover number-theoretical concepts, graphs, combinatorics, discrete probabilities, and models of computation. The student will obtain the fundamental skill of computational thinking and will be better equipped to tackle technical subjects throughout the curriculum. The course is an introduction to discrete mathematics as a foundation to work within the fields of computer science, information technologies, and software development. The course develops the necessary terminology and conceptual tools needed for later courses.

This includes:

• formal reasoning, proofs, logic, set theory, sequences and sums
• number theory, combinatorics and (discrete) probability theory
• induction, recursion and counting
• relations and functions
• basic graph theory, language theory
• theory and models of computation, such as finite state machines, regular expressions and grammars

This course will introduce students to techniques for solving complex programming tasks arising in modern IT systems. The focus in the course is on algorithm design and analysis.

This course addresses advanced programming techniques, with a special attention on functional programming and its applications. The course is a perfect balance of theory and practice, with focus on the Scala programming language.

The course is designed to enable the student to engage with future developments in software engineering by providing a forum for the critical discussion of processes, methods, tools, and architectures. By combining theoretical insights from state-of-the-art research and hands-on experience with advanced tools and techniques, students gain a comprehensive understanding of software engineering.

This course is an advanced course on algorithms which builds on top of an introductory course on algorithms and data structures. The course focuses on advanced techniques for identifying and solving computationally hard problems and on how to adapt such techniques to real-world scenarios.

This course is about that part of programming that focuses on parallelism and concurrency. It covers concurrency concepts that are universal to any programming language. The Java programming language is the main language used for practically addressing such aspects; and Erlang is also used for a topic in the course.

This course introduces key concepts from computing and provides critical and analytical skills for students to engage with a range of computational formats, artifacts, and genres of technical communication. The course enables students to navigate complex technical challenges and meaningfully participate in debates regarding the management of IT. Students learn to understand technical challenges in computing within in organizational, historical, and cultural context. The course is at an introductory level and is well-suited for students with limited or no technical background.

The course will teach students to analyse complexity within an empirical case that explores a current topic within the fields of science, technology, and innovation.

The goal of the course is to address the complex entanglements of IT, innovation and society.

This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.

In this course students will learn to apply modern state-of-the-art solutions for natural language processing problems. We go beyond simple classification tasks, and tackle more advanced types of tasks, like generation and structured prediction. 

Networks are all around us: We are ourselves, as individuals, the units of a network of social relationships of different kinds; the Internet and the highway system can be modelled as networks embedded in space; networks can be also entities defined in an abstract space, such as networks of acquaintances or collaborations between individuals. This course aims at providing the computational tools to study these networks and form an advanced understanding of the current state of the art of network science. The final objective is to have the students master computational techniques to solve advanced network problems, to be able to contribute to the development of network analysis, and to appreciate the limitations and future developments of scientific papers dealing with network problems in real-world data.

Kursets formål er at give en dybere praktisk og teoretisk forståelse for implementation af programmeringssprog, herunder især maskinmodellen bag Java, C# og F#. Kursusemnerne omfatter leksikalsk analyse, grammatikker, syntaksanalyse, abstrakt syntaks, stakmaskiner, virtuelle maskiner, mellemkodesprog (inklusive JVM og .NET CLI), og continuations. Endvidere implementering af fortolkere, typeinferens, oversættere for en delmængde af C og for en delmængde af SML, garbage collectors, og kodegenerering for virtuelle maskiner og maskinkode (fx x86).

This is an introductory course on information security. The course focuses on introductory aspects of analysis, design and implementation of secure software.

The course is an introductory course to machine learning its mathematical foundation.

 We recommend that you do not take this cour if you have already taken the course Machine Learning on the Bachelor programme in Data Science at ITU or a similar.

This hands-on course teaches you how the design, implementation, and implications, of systems software influence your task as an application programmer. You will get an in-depth understanding of how hardware and the operating system work; in the process, you will learn how computers actually work, and what actually happens when you run an application. This enables you to fully exploit underlying hardware and systems software to write high-quality software.

Today we rarely use programs that are developed to fit one specific customer. When we play games, use business systems for administration, or download apps to our phones, we use software that is part of software ecosystems: software products that are configured to fit a specific business context; platforms that allow sharing of specific kinds of software; or software as a service. Examples of software ecosystems have developed around software products, like game engines, or enterprise resource planning (ERP or CRM) software, integrated development environments like visual studio and platforms like the Android operating system. 

In this course you explore software ecosystems from UX design, organizational and technical perspectives. To this end you will analyse a software ecosystem together, propose an improvement related to the ecosystem, and support your proposal with a prototype and / or other design artefacts.

This course enables students to understand the effects of automated digital systems on the way a welfare society is governed and experienced by social groups and individuals.

The course aims at giving the students an introduction to the complexities of determining how processes of digitalisation and green transitions are entangled.

The course aims at giving the students an introduction to the complexities of determining how processes of digitalisation and green transitions are entangled.

Students attending this course will study how to design game code that is scalable and takes into account hardware and software resources.

This course introduces fundamental and advanced concepts in statistics and probability from a data-science perspective. The aim of the course is for the student to be familiarised with probabilistic and statistical methods that are widely used in data analysis.

This course examines organizations in light of contemporary challenges with digitalization. It explores organizational perspectives on digitalization and cover theories that can help us better understand, and critically reflect upon, such profound challenges, while also offering hands-on approaches to managing them. Furthermore, it investigates questions of the changing nature of work, and themes such as leadership, management, and organizing in order to make students both knowledgeable about, and skilled in the practice of, and engaging with, digital transformation.

The course gives an in-depth introduction to fundamental principles of probability theory.

The goal of the course is to teach the understanding, design, implementation, and use of modern artificial intelligence (AI) techniques for generating efficient, intelligent behaviors in games and other simulation environments. A particular focus is given to techniques applied to agent-based simulations.

The course is for students looking for an introduction to strategic management to be able to understand and navigate todays and tomorrows business world.

The student will be given a toolbox of academic frameworks and models to be able to analyze current business situations and propose strategic changes. Through case studies and group work the student will become familiar with the business language which will help to bridge the gap between business and technology.

The overall aim of the course is to enable students to understand and analyse relations between society and technology.

The course presents the students with several perspectives on game analysis and design. The unique aspect of the course is the combination of theoretical and practical approaches. Every unit familiarizes the students with the newest development in game studies and follows it with a presentation by a practitioner. This shows the students the interplay between the academic and industry perspectives. The students must also use the topics raised during the lectures as inspiration for their game creation or their original analyses of game elements.