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 EBUS537
Coordinator Professor D Song
Operations and Supply Chain Management
Dongping.Song@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

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 12

 6

       12

6

 36
Timetable (if known) 60 mins X 1 totaling 12
  		60 mins X 1 totaling 6
  		      60 mins X 1 totaling 12
60 mins X 1 totaling 6
  		 
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
Individual Report There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) : 1  -3500 words    100       
Individual assignment. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) : 1  -1000 words    0       

Aims

To demonstrate in-depth understanding and knowledge of the concepts, theories and developments associated with the subject area, and critically and analytically discuss outcomes in a methodological, structured, logical and in-depth manner;

To demonstrate the ability to apply current tools and techniques of machine learning and data mining in suitable depth and at the appropriate level.

Learning Outcomes

(LO1) Gain an in depth knowledge and principles in the areas of data mining, machine learning, and data warehousing;

(LO2) Critically assess the strengths and weaknesses of various data mining and machine learning techniques from a practitioner/ user perspective;

(LO3) Be able to identify, formulate and solve problems arising from practical applications using data mining and machine learning principles and techniques.

(S1) Adaptability

(S2) Problem solving skills

(S3) Commercial awareness

(S4) Organisational skills

(S5) Communication skills

(S6) IT skills

(S7) International awareness

(S8) Lifelong learning skills

Teaching and Learning Strategies

Hybrid delivery, with social distancing on campus.

1 hour online asynchronous learning per week x 12 weeks
1 hour face-to-face synchronous lecture per week x 12 weeks
1 hour face-to-face seminar every other week x 6 weeks
1 hour face-to-face peer-to-peer learning every other week (unscheduled) x 6 weeks
Self-directed learning x 114 hours

Syllabus
 

Data mining, machine learning, data warehousing: an overview, exploring the main issues and applications of data mining, machine learning, data warehousing in relation to business management;

Data and data measurement: data attributes, data types, measurement, accuracy, data pre-processing;

Data exploration: summary statistics, data visualisation techniques;

Data warehousing: database management system, data warehouse architecture, metadata, data warehousing;

Learning association rules: association rule mining, market basket analysis, types of association rules, methods for mining association rules;

Classification: classification model, techniques for data classification and prediction, decision tree method, practical issues of classification;

Clustering: similarity measure, clustering techniques, issues and applications of clustering;

Other data mining and machine learning techniques: regression, reinforcement learning, optimisation.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.