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Curricular information is subject to change
On successful completion of this module the student will be able to:
1. Understand the principles and the purposes of machine learning.
2. Identify problems which are suitable for the application of machine learning.
3. Retrieve and analyse real-world datasets.
4. Use appropriate machine learning techniques for a given data analytics problem.
5. Apply the process of data understanding and address data quality issues.
6. Design evaluation experiments for selecting the best predictive model for a given problem.
Python programming, data analysis, scientific method
Student Effort Type | Hours |
---|---|
Practical | 60 |
Autonomous Student Learning | 310 |
Total | 370 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Exercises related to lecture material | Varies over the Trimester | n/a | Graded | Yes | 100 |
Resit In | Terminal Exam |
---|---|
Autumn | No |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
Name | Role |
---|---|
Marion Bartl | Lecturer / Co-Lecturer |
Mr Patrick English | Lecturer / Co-Lecturer |
Agatha Carolina Hennigen de Mattos | Lecturer / Co-Lecturer |