| Unit name | Machine Learning and Data Mining for Health Data Science |
|---|---|
| Unit code | BRMSM0060 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Matthew Suderman |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
BRMS_3_datasci |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
N/A |
| School/department | Bristol Medical School |
| Faculty | Faculty of Health Sciences |
This course aims to introduce basic concepts and methods for data mining (discovering patterns in data) and machine learning (constructing prediction models from a data science perspective) including:
By the end of this unit students should be able to:
1. Apply machine learning methods to a range of health data science applications.
2. Discuss the strengths and limitations of machine learning.
3. Assess the performance of a machine learning algorithm.
4. Apply data mining methods to a range of health data science applications.
5. Compare and contrast different data mining methods.
6. Apply methods to interpret machine learning models.
7. Discuss basic concepts of automated causal inference.
• There will be 10 teaching weeks.
• Teaching will include learning activities including lectures, small group work, discussions, individual tasks, and practical sessions.
• Directed and self-directed learning will include activities such as reading, accessing web-based supplementary materials, critical analysis, and completion of assessments.
• 75 hours of directed and self-directed learning. The directed learning includes 25 hours of teaching, and the self-directed learning includes activities such as reading, quizzes, and multi-media learning.
There will be two types of formative assessment. This first formative assessment will take the form of questions and quizzes in lectures and practical sessions and the associated feedback obtained from lecturers/tutors and peers.
The second formative assessment will take the form of a flipped classroom machine learning quiz. In this students will be put into an even number of groups. Each group will write 10 MCQs based upon the content of the module. The groups will then be put into pairs with each group completing the other’s questions. The groups will then provide feedback to each other about the answers.
Summative assessment:
A mark of 50% is required to pass the unit.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BRMSM0060).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the Faculty workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
