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
Description: Lecture (12 Lectures of 2 hours each)
Scheduled Directed Student Hours: 24 hours
Attendance Recorded: Yes
Students will attend the weekly 2 hours lecture during which the key concepts will be introduced.

Teaching Method – Seminar (6 seminars of 2 hour each)
Description: Face to face sessions
Scheduled Directed Student Hours: 12 hours
Attendance Recorded: Yes
The seminar will take place over 6 weeks. During these sessions, students will learn to use python to implement the machine learning tools.

Self-Directed Learning Hours: 114 hours
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings or topics to complete with the expectation that students are well prepared to contribute to the tutorial activities and to understand the content of lectures.

Skills/Other Attribut es Mapping

Skills / attributes: Communication
How this is developed: By contributing to in-class discussions (lectures and seminars), and by preparing the project reports.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: IT Literacy
How this is developed: By using specialist packages from Python, the programming language, in lectures and seminars. The students will also develop their skills by using digital tools and specialist software to engage with the course material, to undertake additional research, and to communicate.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Lifelong Learning
How this is developed: : In lectures and seminars by developing a solid understanding of popular estimation techniques and their usefulness.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Numeracy
How this is developed: During the le ctures & seminars and when working on the projects, the students will be analysing new problems and writing computer codes to analyse datasets.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Problem solving
How this is developed: During the lectures & seminars, the students will gather and synthesise information, compare different estimation methodologies, and use their knowledge of programming, statistics, and finance to formulate a recommendation.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Organization
How this is developed: In lectures & seminars, the students will learn to manage their time carefully by prioritising and completing tasks within specific deadlines. They will also develop their organization skills by working on group and individual projects.
Mode of assessment (if applicable): Group and individual projects

Skills / attributes: Leaders hip
How this is developed: During the lectures & seminars, the students will have the opportunity to lead team activities. For instance, they will have the opportunity to plan the tasks, identify the resources needed to complete the task, monitor the progress of the group and review the plan if needed.
Mode of assessment (if applicable): Group project

Skills / attributes: Teamwork
How this is developed: In lectures & seminars, the students will work in teams to complete the assigned tasks. They will also work together to complete the group project. In doing so, they will understand the importance of teamwork, manage the interaction and relationships with other group members, gain experience in negotiation, persuasion, influencing and managing conflict.
Mode of assessment (if applicable): Group project

Syllabus
 

• Introduction to data mining and machine learning
• Multiple regression model
• Regularisation techniques
• Classification techniques, such as Tree-based methods
• Natural Language Processing, including text mining

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.