| Unit name | Computational Methods for Data Science, Machine Learning and AI |
|---|---|
| Unit code | MATHM0052 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Tadic |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
MATH20800 (Statistics 2), MATH20008 (Probability 2) and MATH20015 (Multivariable Calculus and Complex Functions) or equivalent.
|
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Why is this unit important?
Data Science, Artificial Intelligence (AI) and Machine Learning have seen exciting advances in recent years and are currently reshaping our world, with all of us interacting daily with systems based on these scientific and technical fields. These three overlapping areas rely on well-established Statistical and Probabilistic principles, but their practical implementation requires the development of algorithms and associated efficient computational methods. Essential to these algorithms are numerical integration (e.g. the estimation of an expectation), optimisation (e.g. the minimization of a loss function or the maximization of the log-likelihood) and very often the combination of both (e.g. the minimization or maximization of functions defined through expectations).
At the core of integration are Monte Carlo methods, fascinating probabilistic/statistical techniques suited to the computation of expectations.
Numerical optimization primarily relies on gradient, or Newton-Raphson, type algorithms. However, in numerous situations (e.g. online statistical inference, deep and reinforcement learning), the gradient takes the form of an intractable expectations, which can be estimated with Monte Carlo methods. This gives rise to stochastic gradient and stochastic approximation algorithms.
How does this unit fit into your programme of study
The unit provides a unified and in-depth perspective on modern computational techniques used in computer age Statistical Science, Data Science, AI and Machine Learning. These are essential for the implementation of the algorithms developed in all these fields.
These computational techniques are often mentioned only briefly and in isolation in other statistical units offered in Bristol as their focus is generally on the statistical concepts.
An overview of content
All the algorithms will be illustrated with concrete modern examples.
How will students, personally, be different as a result of the unit
Students will have a unified and coherent perspective on the essential computational techniques used in modern Statistics, Data Science, AI and Machine Learning, allowing them to navigate comfortably the relevant research literature, whether as employed in industry or academia. After taking this unit students will indeed be well placed to pursue research at PhD level for example.
Learning Outcomes
The students will be able to:
Students will also have the opportunity to gain practice in the implementation of algorithms in R or Python.
The unit will be taught through a combination of:
Tasks which help you learn and prepare you for summative tasks (formative):
Weekly problem sheets and computer practicals.
Tasks which count towards your unit mark (summative):
80% timed examination, 20% Coursework.
When assessment does not go to plan
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 (or, in certain scenarios, if you had failed the assessment), you will normally be required to complete it in the reassessment period. Please refer to your official results for the details of your reassessments. The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.
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. MATHM0052).
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 University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
