Close
| Level | Credits | Term | Type |
|---|---|---|---|
| 6 or 7 | 15 | 2 | Departmental |
Module description
This module aims to introduce several fundamental ways by which scalability plays a role in statistical data science, namely large data (both in the number of observations and the number of covariates) and large models (with inferential, engineering and computational implications).
Further details are available in the STAT0043 UCL Module Catalogue entry.
Prerequisite knowledge
STAT0043 is primarily intended for students within the Department of Statistical Science (including the CSML programme). The academic prerequisites for these students, in addition to their (other) compulsory modules, are both of STAT0041 & STAT0042 (UG), or one of COMP0078 / COMP0088 (PGT).
For outside students, the same modules alone are considered sufficient prerequisites, i.e. both of STAT0041 & STAT0042, or one of COMP0078 / COMP0088.
Registration process
STAT0043 is offered as an elective. Prospective elective students who have taken an appropriate combination of the prerequisite modules listed above should simply register for STAT0043 on Portico and await a decision. Prospective elective students who have taken courses equivalent to the listed modules must additionally consult a member of staff in the Department of Statistical Science.
Other considerations
A strong background in linear algebra, calculus in multiple dimensions and the basics of probability is essential. A good overview of the mathematical prerequisites for this module is given in Chapters 1 to 6 of the ‘Mathematics of Machine Learning’ book by M. P. Deisenroth, A. Faisal & C. S. Ong.