Machine Learning
Course Code
IF3270
Number of Credits
3
Semester
6
Course Type
C
Related Courses
| No | Code | Course | Relation |
|---|---|---|---|
| 1 | IF3270 | Machine Learning | Equivalent |
Study Material
| Study Material | Depth |
|---|---|
| CS-IS-10. Advanced Machine Learning | Expert |
| DS-DM-1. Proximity measurement | Express |
| DS-ML-2. Supervised learning | Expert |
| DS-ML-1. General | |
| DS-DM-5. Classification and regression | Expert |
| DS-DM-4. Cluster analysis | Expert |
| DS-DM-2. Data preparation | |
| DS-ML-5. Deep learning | Expert |
| DS-ML-4. Mixed methods | Expert |
| DS-ML-3. Unsupervised learning | Expert |
| CS-IS-4. Basic Machine Learning |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Explain the differences between unsupervised and supervised learning types |
| CPMK 2 | Explain and implement simple algorithms for both types of learning. |
| CPMK 3 | Presenting the results of selecting the appropriate type of learning for a particular problem/application case. |
| CPMK 4 | Conducting an evaluation of the performance of a learning algorithm in a particular problem case. |
Learning Method
- Lectures, Presentations, Project-based study, Group work, Tutorials
Learning Modality
- Online/Offline Synchronous/Asynchronous Independent/Group
Assessment Methods
- Mid-term exams, final exams, quizzes, assignments