FM1AZP110 Exam Project
FM1AZP110 Exam Project
- Course description
- Course codeFM1AZP110
- Level of study5.1
- Program of studyApplied Machine Learning
- Credits10
- Course coordinatorLeon Grobbelaar
The year ends with a major project that reflects the competence candidates have acquired during the academic year. The candidate must solve the assignment independently or in a team with fellow students from a practical problem. The project challenges the candidate to find a real-world project to acquire practical experience in a professional setting. Candidates are responsible for all aspects of the project, in accordance with the supervisor through the internship, if applicable. If the candidate is not working on a real-world project, the teacher will present an alternative case project. The completed project will be presented to the teacher, sensor, fellow students, and, if applicable, the client.
The project challenges candidates to apply and combine accumulated competence from the first and second semesters to demonstrate his/her ability to complete a large project. Candidates are encouraged to collaborate with fellow students across disciplines to hone collaborative skills further.
The candidate:
- has insight into relevant standards for machine learning in relation to project preparation, presentation and delivery
- can update his/her knowledge of programming and machine learning
- understands the importance of the machine learning discipline as a process and method to solve user-centric problems
The candidate:
- can apply knowledge to problems and issues in machine learning projects
- masters relevant tools, techniques and methods to execute machine learning projects based on industry-relevant topics
- can find information and material that is relevant to the project
- can study his/her own project and identify issues and what measures need to be implemented to optimise the results and meet deadlines
The candidate:
- understands the ethical principles that apply in the machine-learning field
- has developed an ethical attitude as a responsible data analyst
- can carry out machine learning tasks based on the needs of target audiences and project briefs, alone or as part of a group
- can build relationships with their peers, also across disciplinary boundaries, and with project owners
- can develop work methods and solutions related to machine learning
In this course, the following teaching and learning methods can be applied, but are not limited to:
- Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
- Group work: Collaborative activities where students work together to solve problems or complete tasks.
- Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
- Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
- Online lessons: Digital content delivered via an online learning platform.
- Guidance: Individualized advice and direction from instructors to support students in their learning journey.
- Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
- Self-study: Independent study where students engage with course material on their own without any teacher support.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Exam Project | Grade A-F | Group/Individual | 8 Week(s) |