UC3MAL10 Machine Learning
UC3MAL10 Machine Learning
- Course description
- NQF LevelBachelor's degree (Level 6 1. Cycle)
- Area of StudyComputing
- Program of StudyApplied Data Science
- ECTS10
- CampusKristiansand, OnlinePLUS - Bergen, OnlinePLUS - Oslo, Online
- Course LeaderIsah Lawal
Language of Instruction and assessment: English
May be offered on Campus and Online.
May be offered as a separate course.
Included in the following bachelor's degrees:
- Applied Data Science
The course aims to explore the approaches used to produce intelligent systems and gain a comprehensive understanding of many of the issues involved and give insight into the mechanisms that allow systems to learn. It will also investigate the deep learning approach to machine learning, introducing students to the concept of deep learning, common and developing algorithms, and associated technologies.
The student has knowledge of
| K1 | the principles of machine learning. |
|---|---|
| K2 | historical and emerging research and developments in the field of machine learning. |
| K3 | the way in which automated processes can self-modify to effect changes in their performance and/or capabilities. |
The student gain skills in
| S1 | design and evaluate representational schemes and the inference mechanisms that use them. |
|---|---|
| S2 | ability to select and apply appropriate deep learning algorithms and technologies to practical domain-relevant challenges. |
| S3 | critically interpret and evaluate the results of applying deep learning to data-driven problems. |
| S4 | evaluate strengths, weaknesses of deep learning algorithms. |
The student can demonstrate
| G1 | the current importance and relevance of Machine Learning within the program of study. |
|---|---|
| G2 | planning, implementing, evaluating and presenting the results of machine learning projects applied to specific problems. |
| G3 | critical reflection on personal academic development and the application of machine learning within problematic situations. |
- Teaching will be based on a hybrid-flexible approach. Instructor-led face-to-face learning is combined with online learning in a flexible course structure that gives students the option of attending sessions in the classroom, participating online, or doing both.
- All activities require active student participation in their own learning.
- Learning delivery methods and available resources will be selected to ensure constructive alignment with course content, learning outcomes and assessment criteria.
- Students will be taught using a mixture of guidance, self-study, and lecture material. Topics will be introduced in a series of weekly lectures. The guidance sessions will be directed practical exercises and reading in which students can explore topics with support from a teacher. This material will also require students to self-manage their time to ensure tasks are completed and the theory is fully understood. This will allow the students to fully engage with lectures and with their peers.
- Learning resources are available in the LMS and include, but is not limited to:
- literature and online reading material (essential and recommended)
- streams, recordings and other digital resources, where applicable
- video conferencing and communication platforms, if applicable
- tools, software and libraries, where applicable
- Students must have access to an internet connection, and suitable hardware.
- Accessing live streams and virtual laboratories requires a minimum broadband connection of 2Mbps (4Mbps recommended).
- Students working on their own laptop/computer are required to acquire appropriate communications software, e.g., webcam, microphone, headphones.
UC1DMA10 Discrete Mathematics, and UC2ADS10 Algorithms and Data Structures, or equivalent course(s).
The reading list for this course and any additional electronic resources will be provided in the LMS.
| Activity | Duration |
|---|---|
Teacher-led activity | 24 |
Teacher-supported work | 48 |
Self-study | 178 |
This course has two (2) exams contributing towards the overall and final grade of the course.
All exams must be assessed as passed to receive the final Course Grade.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Online Exam | A-F | ||
Report | A-F |