ULMS Electronic Module Catalogue |
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
Title | Data Mining and Machine Learning | ||
Code | ACFI331 | ||
Coordinator |
Dr C Wese Simen Finance and Accounting C.Wese-Simen@liverpool.ac.uk |
||
Year | CATS Level | Semester | CATS Value |
Session 2025-26 | Level 6 FHEQ | First Semester | 15 |
Pre-requisites before taking this module (other modules and/or general educational/academic requirements): |
ACFI232 Database Management; ACFI233 Econometrics for Finance II; ACFI235 Financial Data Visualisation |
Modules for which this module is a pre-requisite: |
Programme(s) (including Year of Study) to which this module is available on a required basis: |
Programme(s) (including Year of Study) to which this module is available on an optional basis: |
Teaching Schedule |
Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
Study Hours |
24 |
12 |
36 | ||||
Timetable (if known) |
120 mins X 1 totaling 24
|
120 mins X 1 totaling 12
|
|||||
Private Study | 114 | ||||||
TOTAL HOURS | 150 |
Assessment |
||||||
EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
Assessment 1: Group Project Assessment Type: Coursework Weighting: 50 % Size: 2000 Words Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Assessm | 0 | 50 | ||||
Assessment 2: Individual Project Assessment Type: Coursework Weighting: 50 % Size: 2000 Words Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous As | 0 | 50 |
Aims |
|
This module aims to equip students with the ability to understand and implement cutting-edge techniques in the area of data mining and machine learning. Upon successful completion of the module, the students should be able to critically evaluate different techniques. Students will be able to propose and implement a methodology to tackle complex financial problems. Furthermore, the students should be able to formulate recommendations based on their interpretation of the results. |
Learning Outcomes |
|
(LO1) Students will be able to compare and contrast different estimation techniques |
|
(LO2) Students will be able to formulate and implement the appropriate methodology to solve a problem |
|
(LO3) Students will be able to produce a set of recommendations based on the results of the implementation |
|
(LO4) Students will be able to propose improvements to the empirical analyses |
|
(S1) Communication |
|
(S2) IT Literacy |
|
(S3) Lifelong Learning |
|
(S4) Numeracy |
|
(S5) Problem Solving |
|
(S6) Organisation |
|
(S7) Leadership |
|
(S8) Teamwork |
Teaching and Learning Strategies |
|
Teaching Method - Lectures Teaching Method – Seminar (6 seminars of 2 hour each) Self-Directed Learning Hours: 114 hours Skills/Other Attribut es Mapping Skills / attributes: Communication Skills / attributes: IT Literacy Skills / attributes: Lifelong Learning Skills / attributes: Numeracy Skills / attributes: Problem solving Skills / attributes: Organization Skills / attributes: Leaders
hip Skills / attributes: Teamwork |
Syllabus |
|
• Introduction to data mining and machine learning Python programming language will be used to implement data mining and machine learning techniques for financial applications. |
Recommended Texts |
|
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |