Opening the Machine Learning black box

Module overview

The module aims to equip you with the necessary foundations to make sense of machine learning applications which may appear as block boxes without such understandings.

Learning and Teaching

Teaching and learning methods

The programme employs a range of teaching and learning methods tailored to online delivery and the needs of working professionals. One of the primary methods used is asynchronous learning, where students can access materials on their own schedule. This includes multimedia resources - but not just video lectures, but also podcasts, animations, and interactive simulations; and reading materials like PDFs or e-books. These resources allow learners to engage with content at their own pace. In addition, discussion forums provide a space for students to ask questions and participate in debates with their peers without the need for everyone to be online at the same time. The asynchronous learning is complemented by synchronous components, such as webinars. These sessions, typically held via Microsoft Teams, give students the opportunity to interact with instructors in real-time, asking questions or participating in discussions. All of these methods are designed to accommodate different learning approaches and ensure that students can apply theoretical knowledge to practical scenarios relevant to their professional contexts. With a strong emphasis on self-paced learning, supported by ongoing instructor guidance.

Study time
Type	Hours
Independent Study	90
Guided independent study	34
Online Course	26
Total study time	150

Resources & Reading list

Journal Articles

Breiman, L. (2001). Statistical modelling: The two cultures.. Statistical Science, pp. 16:199-231.

Bradley Efron (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, pp. 115:636-655.

Textbooks

Friedman, J., Hastie, T. and Tibshirani, R. (2017). The elements of statistical learning. Springer.

James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Coursework	100%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Coursework	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Coursework	100%

Repeat Information

Repeat type: Internal & External