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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.
Resit In | Terminal Exam |
---|---|
Autumn | No |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.
Name | Role |
---|---|
Agatha Carolina Hennigen de Mattos | Lecturer / Co-Lecturer |
Dr Arjumand Younus | Lecturer / Co-Lecturer |
Lecture | Offering 51 | Week(s) - 39, 40 | Fri 10:00 - 15:50 |
Lecture | Offering 51 | Week(s) - 39, 40 | Mon 10:00 - 15:50 |
Lecture | Offering 51 | Week(s) - 39, 40 | Thurs 10:00 - 15:50 |
Lecture | Offering 51 | Week(s) - 39, 40 | Tues 10:00 - 15:50 |
Lecture | Offering 51 | Week(s) - 39, 40 | Wed 10:00 - 15:50 |