Machine Learning & AI
Applied modelling, preprocessing, feature engineering, deep learning, statistics, computational intelligence, and evaluation.
Academic foundation
B.Sc. Computer Technology + Applied Machine Learning
A curated overview of the technical foundation behind my project work: software engineering, algorithms, databases, systems, security, statistics, and applied machine learning.
Degree context: My academic background combines a Bachelor of Science in Computer Technology from the University of Bergen with additional studies in Applied Machine Learning. The degree covered programming, object-oriented design, databases, algorithms and data structures, systems development, networks, functional programming, software security, concurrency, programming languages, information systems, statistics, cybersecurity, physics, and operating systems.
Technical areas
I organize my academic work by skill area rather than by a long chronological list of courses. This makes it easier to see how the coursework connects to practical project work.
Software Engineering
Object-oriented programming, modular design, team-based software development, testing, debugging, version control, and maintainable code.
Data & Databases
SQL, relational database design, information systems, data modelling, structured storage, and data pipeline thinking.
Security & Systems
Software security, cybersecurity, operating systems, computer networks, concurrency, secure programming, and lower-level system behaviour.
Algorithms & Programming Languages
Algorithms, data structures, graph algorithms, recursion, functional programming, language semantics, parsers, interpreters, and type systems.
Web, APIs & Interfaces
Web fundamentals, dashboards, APIs, project presentation, and translating technical outputs into usable interfaces.
Selected coursework
A compact overview of courses grouped by the type of technical skill they strengthened.
| Area | Selected courses | Skills developed | Connected project work |
|---|---|---|---|
| Programming & Software | Introduction to Programming, Object-Oriented Programming, Systems Development | OOP, modular design, team development, testing, debugging, Git, maintainability | F1 telemetry API, portfolio site, Aker Solutions dashboard |
| Algorithms & CS Fundamentals | Algorithms and Data Structures, Discrete Structures, Functional Programming, Programming Languages | Graph algorithms, complexity, recursion, Haskell, parsers, interpreters, type systems | Reusable code structure, algorithmic problem-solving, modelling logic |
| Data & Information Systems | Databases and Modelling, Information Systems, Statistics | SQL, relational modelling, data organization, statistical reasoning, information-system context | PostgreSQL telemetry platform, GeoRisk feature tables |
| Security & Systems | Software Security, Cybersecurity, Computer Networks, Operating Systems, Concurrent Programming | Secure software thinking, networks, OS concepts, concurrency, reliability, risk awareness | Production-minded software practices, API/security awareness |
| Applied ML & Modelling | Applied Machine Learning, Deep Learning, Metaheuristics, Data Mining | Preprocessing, model training, evaluation, optimization, ML workflows | GeoRisk, F1 tyre degradation, strategy simulation |
How this connects to my projects
My strongest projects combine the breadth of the computer technology degree with applied ML. GeoRisk uses data processing, geospatial feature engineering, modelling, evaluation, and risk communication. The F1 telemetry platform uses APIs, databases, time-series processing, and reusable software structure.
How I use this foundation
I try to build projects that go beyond notebooks: structured code, documented assumptions, reproducible workflows, practical outputs, and clear limitations. This is the same mindset I want to bring into applied AI and data systems work.