AI and Computer Vision
Specialisation:
AI and Computer Vision
Specialisation Coordinator
This specialisation gives you an in-depth theoretical understanding of the mathematical models and statistical methods that form the basis of artificial intelligence. You will learn to develop advanced AI solutions as well as to train computer models to solve complex tasks.
You will gain competences in collecting, cleaning and analysing large amounts of data, and you will gain knowledge about how to train computers to understand and interpret visual information from images and videos. You will also learn basic techniques such as pattern recognition and deep neural networks, and you will gain an understanding of the algorithms and mathematics behind machine learning, image processing and object recognition.
As a graduate MSc in Engineering specialised in AI & Computer Vision, you will work with computer technologies that can make decisions autonomously or in interaction with humans. Your expertise will make you highly sought-after by the labour market, and you can look forward to working with cutting-edge technologies.
Structure
To tailor your specialisation, you should be aware of the following study elements:
- Mandatory Courses: You need to have all 20 ECTS.
- Specialisation Core Courses: You need to select 25 ECTS of these. You can choose to have any ECTS points exceeding this limit count as either Specialisation Elective Course or Elective Course.
- Specialisation Elective Courses: You need to select 15 ECTS of these (max 10 ECTS can be at bachelor's level). Any ECTS points exceeding this limit count as Elective Course.
- Elective Courses: You need to select 30 ECTS of these (max 10 ECTS can be at bachelor's level). You can choose among 1) the Specialisation Core Courses, 2) the Specialisation Elective Courses, 3) the courses mentioned under Study Contract Recommendations, and 4) the courses mentioned under Alternative Elective Courses.
- MSc Thesis: This counts for 30 ECTS.
All these elements add up to 120 ECTS, i.e. 4 semesters of 30 ECTS.
When you tailor your specialisation, please be aware of the prerequisites stated in the course descriptions. Most courses are designed with progression in mind, i.e. some courses are intended to be studied before others. Also, check timetable.au.dk for potential course schedule overlaps.
Schedule for Winter Start
This is a proposed structure of the programme. Elective courses and specialisation electives may be rearranged as needed.
| 1(S) | Systems Engineering | Innovation & Entrepreneurship | Computer Vision | Explainable Statistical Learning | Information Theory | |
|---|---|---|---|---|---|---|
| 2(F) | Research Methodology | Security & Privacy | Deep Learning | Advanced Signal Processing | ||
| 3(S) | Elective | Elective | Elective | Elective | Elective | Elective |
| 4(F) | Master Thesis | |||||
Schedule for Summer Start
This is a proposed structure of the programme. Elective courses and specialisation electives may be rearranged as needed.
| 1(F) | Research Methodology | Security & Privacy | Deep Learning | Advanced Signal Processing | ||
|---|---|---|---|---|---|---|
| 2(S) | Systems Engineering | Innovation & Entrepreneurship | Computer Vision | Explainable Statistical Learning | Information Theory | |
| 3(F) | Elective | Elective | Elective | Elective | Elective | Elective |
| 4(S) | Master Thesis | |||||
Specialisation electives
Specialisation Core Courses: You need to select 25 ECTS of these. You can choose to have any ECTS points exceeding this limit count as either Specialisation Elective Course or Elective Course.
Specialisation Elective Courses: You need to select 15 ECTS of these (max 10 ECTS can be at bachelor's level). Any ECTS points exceeding this limit count as Elective Course.
Spring
| Course Specialisation Type | Course Title | ECTS | Semester | Level |
| Core course | Computer vision | 10 | Spring | M |
| Core course | Explainable statistical learning | 5 | Spring | M |
| Specialisation elective course | Digital image processing | 5 | Spring | B |
| Specialisation elective course | Information theory: From communication to learning | 5 | Spring | M |
| Specialisation elective course | Research and Development project in Computer Engineering | 5 | Spring/Autumn | M |
Fall
| Course Specialisation Type | Course Title | ECTS | Semester | Level |
| Core course | Advanced signal processing | 10 | Autumn | M |
| Core course | Deep learning | 10 | Autumn | M |
| Specialisation elective course | Medical image analysis | 5 | Autumn | M |
| Specialisation elective course | Research and Development project in Computer Engineering | 5 | Spring/Autumn | M |
| Specialisation elective course | Statistical learning and machine learning | 10 | Autumn | B |
| Specialisation elective course | Stochastic signal processing | 5 | Autumn | B |
Recommended electives in the Specialization
Study Contract Recommendations
If you choose any of the non-ECE courses, you should approach the Specialisation Coordinator for a brief alignment of expectations and approval. Even if the course is listed as recommended, it might not be suited for your specialisation, it may be hard to secure a seat, or it might not be straightforward to transfer the ECTS credits.
To be acquainted with the staff and the research at our Department, and to gather inspiration for your upcoming MSc. Thesis, you should consider (max 10 ECTS including any Research and Devlopment Project done as a Specialisation Elective Course):
- Research and Development Project in Computer Engineering, MSc, 5/10 ECTS, Spring/Autumn, ECE
- Research and Development Project in Computer Engineering with Industry, MSc, 5/10 ECTS, Spring/Autumn, ECE
- Computer Engineering Study Course A/B, MSc, 5 ECTS, Spring/Autumn, ECE
Spring
Recommended Elective Courses mainly within AI:
- Tiny Machine Learning, BEng, 5 ECTS, Spring, ECE
- Human-Centered AI, MSc, 10 ECTS, Spring, CS (AI Specialization, CS)
Recommended Elective Courses mainly within Computer Vision and Image Processing:
- Only in Autumn
Recommended Elective Courses mainly within Signal Processing:
- Sound and Acoustics, BEng, 5 ECTS, Spring, ECE
- Discrete-time Signal Processing, BSc, 5 ECTS, Spring, ECE
- Embedded Signal Processing, BEng, 5 ECTS, Spring, ECE
Recommended Elective Courses with general applicability for the specialisation:
- Optimization, BSc, 10 ECTS, Spring, CS
- Large Scale Optimization, MSc, 10 ECTS, Spring, Data Science
Alternative Elective Courses
You are also free to browse the AU Course Catalogue on your own. If you consider choosing any of these alternative Elective Courses, you should approach the Specialisation Coordinator for a brief alignment of expectations and approval. Even if the course is in the AU Course Catalogue it might not be suited for your specialisation, it might be hard to secure a seat, or it might not be straightforward to transfer the ECTS credits.
Alternative Elective Courses mainly within AI:
- Machine Learning, BEng, 5 ECTS, Spring, ECE
- Data Mining, MSc, 10 ECTS, Spring, CS (AI Specialization, CS)
- Cluster Analysis, MSc, 10 ECTS, Spring, CS (AI Specialization, CS)
- Randomized Algorithms, MSc, 10 ECTS, Spring, CS (AI Specialization, CS)
- Statistical Inference for High Dimensional Data, MSc, 10 ECTS, Spring, Data Science
- Modelling and Solving Optimisation Problems, BSc, 10 ECTS, Spring, Data Science
- Customer Analytics, MSc, 10 ECTS, Spring, Econ.-BA
- Business Data Analysis, MSc, 10 ECTS, Spring, Econ.
- High-Performance Computing for Data Analysis, MSc, 5 ECTS, Spring, Bioinformatics
Alternative Elective Courses mainly within Computer Vision and Image Processing:
- Currently none
Alternative Elective Courses mainly within Signal Processing:
- Autonomous Mobile Robotics, BSc, 5 ECTS, Spring, ECE
- Biomedical Signal Processing and Machine Learning, MSc, 10 ECTS, Spring, ECE
- SW3DSB-01 Digital Signal Processing, BEng, 5 ECTS, Spring, ECE
- E3DSB-01 Digital Signalprocessing, BEng, 5 ECTS, Spring, ECE
- Applied Time Series Econometrics, MSc, 10 ECTS, Spring, Econ.
- Advanced Applied Time Series Econometrics, MSc, 10 ECTS, Spring, Econ.
Alternative Elective Courses with general applicability for the specialisation:
- Mixed Integer Optimization, MSc, 10 ECTS, Spring, Math-Econ
- Applied Optimization: Vehicle Routing, MSc, 10 ECTS, Spring, Math-Econ
- Introduction to Sampling, MSc, 5 ECTS, Spring, Statistics
Fall
Recommended Elective Courses mainly within AI:
- Tiny Machine Learning, BEng, 5 ECTS, Spring, ECE
- Human-Centered AI, MSc, 10 ECTS, Spring, CS (AI Specialization, CS)
- Natural Language Processing (NLP), MSc, 10 ECTS, Autumn, CS (AI Specialization, CS)
Recommended Elective Courses mainly within Computer Vision and Image Processing:
- Deep Learning for Visual Recognition, MSc, 10 ECTS, Autumn, CS (AI Specialization, CS)
- Visual Computing: Interactive Computer Graphics and Vision, MSc, 10 ECTS, Autumn, CS
Recommended Elective Courses mainly within Signal Processing:
- Sound and Acoustics, BEng, 5 ECTS, Spring, ECE
- DSP in Audio Technology and Engineering, BEng, 5 ECTS, Autumn, ECE
- Discrete-time Signal Processing, BSc, 5 ECTS, Spring, ECE
- Embedded Signal Processing, BEng, 5 ECTS, Spring, ECE
- Time Series Econometrics, MSc, 10 ECTS, Autumn, ECON
Recommended Elective Courses with general applicability for the specialisation:
- Data Visualization, MSc, 10 ECTS, Autumn, CS (AI Specialization, CS)
- Optimization, BSc, 10 ECTS, Spring, CS
- Convex Optimization, BSc, 5 ECTS, Autumn, Math-Econ
- Large Scale Optimization, MSc, 10 ECTS, Spring, Data Science
- Linear Optimization, BSc, 10 ECTS, Autumn, Math-Econ
Alternative Elective Courses
You are also free to browse the AU Course Catalogue on your own. If you consider choosing any of these alternative Elective Courses, you should approach the Specialisation Coordinator for a brief alignment of expectations and approval. Even if the course is in the AU Course Catalogue it might not be suited for your specialisation, it might be hard to secure a seat, or it might not be straightforward to transfer the ECTS credits.
Alternative Elective Courses mainly within AI:
- Machine Learning, BSc, 10 ECTS, Autumn, CS
- Theoretical Foundations of Machine Learning, MSc, 10 ECTS, Autumn, CS (AI Specialization, CS)
- Advanced Data Management and Analysis, MSc, 10 ECTS, Autumn, CS (AI Specialization, CS)
- Algorithms, Incentives, and Data, MSc, 10 ECTS, Autumn, CS (AI Specialization, CS)
- Statistical Data Analysis using Python, BSc, 5 ECTS, Autumn, Data Science
- Advanced Statistical Learning, MSc, 10 ECTS, Autumn, Data Science
- Reinforcement Learning, MSc, 10 ECTS, Autumn, Data Science
- Causal Inference, BSc, 10 ECTS, Autumn, Data Science
- Artificial Intelligence and Business, MSc, 5 ECTS, Autumn, Econ.-BA
- Business Forecasting, MSc, 5 ECTS, Autumn, Econ.-BA
- Business Intelligence, MSc, 5 ECTS, Autumn, Econ.-BA
- Generative AI with LLMs, MSc, 5 ECTS, Autumn, Econ.-BA
- Data Science in Bioinformatics, MSc, 10 ECTS, Autumn, Bioinformatics
Alternative Elective Courses mainly within Computer Vision and Image Processing:
- Currently none
Alternative Elective Courses mainly within Signal Processing:
- Advanced Control Systems, BEng, 5 ECTS, Autumn, ECE
- Signals and Systems, BSc, 5 ECTS, Autumn, ECE
- EE3DSB-02 Digital Signal Processing, BEng, 5 ECTS, Autumn, ECE
- EEH3DSB-01 Digital Signal Processing 1, BEng, 5 ECTS, Autumn, ECE
- EH5ADSB-01 Advanced Digital Signal Processing, BEng, 5 ECTS, Autumn, ECE
- ETASP-01 Adaptive Signal Processing, BEng, 5 ECTS, Autumn, ECE
- SW3DSB-01 Digital Signal Processing, BEng, 5 ECTS, Autumn, ECE
Alternative Elective Courses with general applicability for the specialisation:
- Multiple Criteria Optimization, MSc, 10 ECTS, Autumn, Math-Econ
- Quantum Information Processing, MSc, 10 ECTS, Autumn, CS
- Theory of Algorithms and Computational Complexity, MSc, 10 ECTS, Autumn, CS
- Statistical Models, BSc, 10 ECTS, Autumn, Math
