Download Data Mining and Machine Learning Paper Code: COMP809 POINTS

SCHOOL OF COMPUTER AND MATHEMATICAL SCIENCES
Paper Title: Data Mining and Machine Learning
Paper Code: COMP809
POINTS:
15
LEVEL:
8
PREREQUISITE/S:
None
COREQUISITE/S:
COMP811
STUDENT LEARNING HOURS:
The learning hours are a guide to the total time needed for a student to complete the paper:
On-Campus Sessions
21
129
Student Directed Learning
Total learning hours
150
PRESCRIPTOR:
Studies and evaluates data mining techniques such as Decision Tree classifiers, Bayesian
classifiers, Apriori techniques for discovering associations between features, clustering algorithms,
and neural network technology. Critically analyses the link between traditional statistical analysis
and data mining.
LEARNING OUTCOMES:
On successful completion of this paper students will be able to:
1. Gain an appreciation of the role that Data Mining plays in enhancing the decision making
process.
2. Gain an understanding of the fundamental concepts that underpin all Mining schemes,
namely, Entropy, Classification, Association and Clustering.
3. Understand the technical issues involved in extracting useful and interesting patters from
large data sets.
4. Conceptualize the entire Mining life cycle from: Problem Definition through to Mining,
Validation, Deployment and back.
5. Evaluate and compare different Mining schemes for solving a given problem.
6. Understand the role that statistical techniques plays in interpreting and evaluating the
accuracy of results produced by different Mining schemes.
7. Conduct a critical appraisal of two major schemes, identifying their strengths and
weaknesses with a view to assessing their suitability in a Real World context.
CONTENT
 The role of Data Mining, Formal Definition, Applications, Concepts of Classification and
Association, General Approaches to Data Mining
 Association Rules, Decision Trees, Nearest-Neighbour Classification, Clustering
 Algorithmic Techniques: The 1-R method, Statistical Methods such as Bayesian Inference
and Linear Regression, Use of Entropy in Decision Tree Construction, Trees versus
Rules, the Apriori Algorithm for Mining Association Rules from large Data Sets
 Use of Data for Training Classifiers, Error Estimation using Confidence Limits, Error
Metrics, use of LIFT charts, ROC Curves
 Detailed treatment of Mining algorithms such as C4.5 and PRISM.
 Neural Network Classification Schemes
LEARNING & TEACHING STRATEGIES
COMP809_2015_desc.doc
Approved by BOS: 26 Feb 2015
Valid From: 01/01/15
Page 1 of 2
The aim is to develop a learning environment of researchers and professionals where practical
application of knowledge is valued and critical reflection is promoted.
Many concepts will be developed through student research; problem based learning,
discussion, practical exercises and analysis.
Other teaching/learning approaches used to encourage the development of student capabilities
include:
 personal reading and research assignments
 critical analysis and reporting
 lectures, guest lectures, and mini lectures
 discussion sessions
 practical software design and development projects and mini projects
The learning and teaching throughout the course will be supplemented by on-line learning
support, intended to enable information sharing, and with activities designed to sustain an
active community of learners.
ASSESSMENT PLAN
Assessment Event
Weighting %
Learning
Outcomes
Assignment 1 – Literature Survey of Data Mining
Applications in Industry
30%
1,2,4
Assignment 2 - Data Mining Project
70%
2,3,5,6,7
Grade Map
Grade Map 1:
A+
A
B+
B
C+
C
D
ABC-
Pass with Distinction
Pass with Merit
Pas
s
Fail
Overall requirement/s to pass the paper:
To pass the paper, the student needs to obtain a minimum of 50%/C- overall.
READINGS
Prescribed Text
No prescribed text
Recommended reading lists will be provided
COMP809_2015_desc.doc
Approved by BOS: 26 Feb 2015
Valid From: 01/01/15
Page 2 of 2