Machine Learning
2020/2021
Content, progress and pedagogy of the module
Learning objectives
Knowledge
Key models in machine learning and their associated learning and inference techniques, such as:
- Statistical linear models
- Markov chains and hidden Markov models
- Support Vector machines
- Neural Net
- Probabilistic Graphic Models
- Matrix factorization
The use of machine learning methods in selected fields of application, such as:
- Web and network mining
- Recommendation Systems
- Computer games
- Image analysis
- Text mining
Skills
- be able to apply advanced techniques from machine learning to the construction of intelligent systems
Competences
- to understand advanced machine learning methods for designing intelligent systems
- to analyze their usefulness and impact in solving specific tasks
Type of instruction
The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.
Extent and expected workload
It is expected that the student uses 30 hours per ECTS, which for this activity means 150 hours
Exam
Exams
| Name of exam | Machine Learning |
| Type of exam | Written or oral exam |
| ECTS | 5 |
| Assessment | 7-point grading scale |
| Type of grading | Internal examination |
| Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Additional information
Contact: The Study board for Computer Science at cs-sn@cs.aau.dk or 9940 8854
Facts about the module
| Danish title | Maskinlæring |
| Module code | DSNCSITK225 |
| Module type | Course |
| Duration | 1 semester |
| Semester | Spring
|
| ECTS | 5 |
| Language of instruction | English |
| Empty-place Scheme | Yes |
| Location of the lecture | Campus Aalborg |
| Responsible for the module |
Organisation
| Study Board | Study Board of Computer Science |
| Department | Department of Computer Science |
| Faculty | Technical Faculty of IT and Design |
