Christian Kårbø Engelsen

Academic

Bergen, Norway

Master's degree in information science (2026) with a background in cognitive science and artificial intelligence. Experience with research methodology, text and network analysis, master's thesis work and academic communication.

Education

Information Science

University of Bergen Master's Degree

120 ECTS credits August 2024 — June 2026

Specialization in information architecture, user behaviour and digital communication.

Cognitive Science

University of Bergen Bachelor's Degree

180 ECTS credits August 2018 — June 2024

Consciousness, cognitive abilities and philosophy of AI. Specialization in computer science, philosophy and information science.

Artificial Intelligence

University of Bergen Bachelor's Degree

80 ECTS credits August 2022 — June 2024

Artificial agents, machine learning and development of AI solutions. Combines computer science and information science.

Coursework

Total 350 ECTS credits

Bachelor's in Cognitive Science

180 ECTS credits

Mandatory

Biological and Cognitive Psychology

The biological foundations of the nervous system, emotions, and learning, combined with cognitive theories of perception, memory, and reasoning.

Deduction and Metalogic

Natural deduction for propositional and first-order predicate logic with identity, model theory, and set theory. Includes metalogic and proofs of properties of formal proof systems.

Examen Facultatum, Basic Linguistics

Introduction to phonetics, phonology, morphology, syntax, and semantics. Provides a theoretical and practical foundation for language-related studies.

Examen Philosophicum

Philosophy of science, ethics, and fundamentals of logic and argumentation theory. Develops critical and analytical competence relevant to one's own field of study.

Functional Programming

The functional paradigm with recursion, higher-order functions, and immutable data structures. Programming in a functional language such as Haskell.

Haskell

Introduction to Cognitive Science

Interdisciplinary overview of cognition from computer science, linguistics, psychology, and philosophy. Covers knowledge representation, reasoning, learning, language, perception, and consciousness.

Introduction to Philosophy of Mind

Modern philosophy of mind and the relationship between mental and physical properties. Covers behaviorism, identity theory, functionalism, and the problem of consciousness.

Introduction to Formal Logic

Introduction to propositional logic and first-order predicate logic. Emphasis on translating natural language into formal logic, truth tables as a proof system in propositional logic, and the tree method in propositional and predicate logic. Provides a foundation for further study in cognitive science.

Introduction to Programming

Practical introduction to programming using a modern language. Covers variables, expressions, control flow, arrays, and file handling.

Python

Knowledge Representation and Reasoning

Logic-based knowledge representation and various forms of reasoning including deductive, default, and abductive reasoning. Also covers reasoning about knowledge, action, and change in multi-agent systems.

Prolog

Object-Oriented Programming

Continuation of programming with focus on code quality. Covers abstractions, object-oriented design, inheritance, polymorphism, generics, and testing.

Java

Language and Cognition

Cognitive science related to linguistic knowledge, language acquisition, and language use. Explores the psychological and cognitive aspects of language.

Statistics for Cognitive Science

Statistical methods and analysis for cognitive science. Covers descriptive statistics, hypothesis testing, and data analysis for cognitive experiments.

Specialization

Advanced Programming

Data structures, algorithms, object-orientation, thread programming, and efficiency analysis. Also covers standards for structured data such as JSON, RDF, and XML.

Python

Data Management

Theory and practice of relational and NoSQL databases. Covers data modeling, normalization, query languages, and indexing principles.

SQL

Formal Methods for Information Science

Elementary logic and set theory, relations, functions, graphs, and trees. Covers combinatorics, probability, information theory, and computability.

Introduction to Artificial Intelligence

Theoretical and practical foundations of AI including state-space search, knowledge representation, inference, and machine learning.

PythonSearch Algorithms

Machine Learning

Supervised learning with deep learning, unsupervised learning including clustering, and reinforcement learning. Practical applications in data analysis.

PythonMachine Learning

Elective

Introduction to Innovation and Entrepreneurship

Innovation and entrepreneurship theory with practical methodology. Development of business models through participatory design and concept development.

Bachelor's in Artificial Intelligence

80 ECTS credits

Also counts towards this degree:

Formal Methods for Information Science INFO104 · Spring 2023 10
Introduction to Artificial Intelligence INFO180 · Autumn 2020 10
Introduction to Programming INF100 · Autumn 2018 10
Knowledge Representation and Reasoning INFO282 · Autumn 2019 10
Machine Learning INFO284 · Spring 2021 10

Mandatory

Ethics in Artificial Intelligence

Explainable AI (XAI), fairness, algorithmic accountability and trust, artificial morality, and responsible use of AI. Includes practice in popular dissemination of AI.

AI Ethics

Introduction to Artificial Intelligence

Broad introduction to artificial intelligence as a discipline, including historical development, ethical implications, and key subfields such as machine learning, robotics, and natural language understanding.

Artificial Agents

Introduction to artificial agents and multi-agent systems, including agent modeling, autonomous agent architecture, control systems, and basic robotics.

PythonROS2

Master's in Information Science

120 ECTS credits

Mandatory

Master's Thesis in Information Science

Independent research project under supervision on a relevant topic in information science. Demonstrates advanced knowledge and scientific methodology, including problem formulation, literature review, data collection, analysis and critical evaluation.

Elective

Research Topic in Artificial Intelligence

Advanced topics in AI including artificial life, multi-agent systems, machine learning, neural networks, genetic algorithms, and natural language processing.

PythonMachine Learning

Research Topic in Networks and Text Analysis

Methods for analyzing text and network data, including language models, document clustering, supervised machine learning, and network models.

PythonNLP

Research Topics in Human-Computer Interaction

Introduction to the HCI research field. Covers advanced concepts and approaches for analyzing and designing interactive systems.

Logic for Multi-Agent Systems

Formal logics for reasoning about multi-agent interaction, including epistemic logic, temporal logic, coalition logic, and alternating-time temporal logic.

Additional courses

20 ECTS credits

Self-chosen Informatics Project I

Self-chosen project work in informatics carried out with a supervisor. Project: analyzing programming language standardization and evolution processes. Includes programming, documentation in the form of a technical report, and an oral presentation.

Informatics Project II

Advanced project work in informatics carried out with a supervisor. Includes programming, documentation in the form of a technical report, and an oral presentation.

PythonNLP

Replaced courses

Formal Methods for Information Science INFO102 · Spring 2029 5 Replaced by INFO104
Machine Learning INFO283 · Autumn 2019 5 Replaced by INFO284

Featured Projects

Personal Website

My personal website and blog, built with SvelteKit.

SvelteKitTypeScriptCSS

Hugo

Source code Live demo

Skills

AI & Data Science

Machine LearningNLPRoboticsSearch AlgorithmsAI EthicsAI SafetyAI Safety GovernanceAI Safety Policy

General

Collaborative workAcademic Writing

Languages

Norwegian (Bokmål) Native

English Fluent

German Basic