Download Computer Engineering MA, Data mining, 7.5 Credits

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Course Syllabus:
Computer Engineering MA, Data mining, 7.5 Credits
General data
Code
DT044A
Subject/Main field
Computer Engineering
Cycle
Second cycle
Credits
7.50
Progressive specialisation
Second cycle, has only first-cycle course/s as entry
requirements
Answerable department
Faculty of Science, Technology and Media
Established
2014-11-24
Date of change
2017-05-06
Version valid from
2017-07-01
Aim
The student should develop a basic understanding of current machine learning
techniques for mining large quantities of data. The student should develop skills in
finding interesting patterns and building prediction models by explorative data
analysis using data analysis tools such as R, Weka or Orange, and preparing input,
interpreting output and critically evaluating results. The student should show an
ability to apply the skills in a small project in an real-world business or
engineering application area such as big data visualization, business intelligence
analysis, decision support systems, text/web/sensor/geo data mining, context
aware applications, intelligent agents or cognitive radio.
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Course objectives
The student should be able to:
ȊȱDiscuss what real-world applications of data mining that are realistic and ethical
ȊȱMine data using a tool such as the R script language, the Python Orange library,
the Weka Java based tool or own implementations of algorithms
ȊȱPrepare input, interpret output and evaluate results
ȊȱIdentify influential variables in a multivariate data set
ȊȱDiscover patterns by association rule mining and evalute their reliability
ȊȱDevelop and validate prediction models
ȊȱFollow a standard methological process reliable problem analysis, modelling and
evaluation
ȊȱApply data mining techniques on a small real-world problem
Content
ȊȱApplication areas of data mining
ȊȱData and knowledge representation (relations, attributes, sparse data, tables,
decision trees, rules)
ȊȱBayesian statistics
ȊȱAssociative and sequential patterns
ȊȱBasic algorithms
ȊȱData clustering
ȊȱData categorization
ȊȱData cleaning
ȊȱData visualization
ȊȱAssociation rules
ȊȱData prediction
ȊȱLaboratory exercise on the R, Orange or the Weka data analysis tool
ȊȱProject
Entry requirements
Computer Engineering BA (AB) including Databases, modeling and
implementation, 6 credits and Java, 6 credits. Mathematics BA (A), 30 credits,
including Mathematical Statistics, 6 credits.
Total previous studies 120 hp.
Selection rules and procedures
The selectionprocess is in accordance with the Higher Education Ordinance and
the local order of admission.
Teaching form
The course may be offered as a campus course or as a web-based distance course.
The student time commitment is estimated to about 200 hours.
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Examination form
0.0 Credit, I101: Choice of project
Grades: Pass or Fail
0.5 Credit, L101: Laboratory exercise
Grades: Pass or Fail
3.5 Credits, T101: Exam
Grades: A, B, C, D, E, Fx and F. A-E are passed and Fx and F are failed.
3.5 Credits, P101: Project presentation
Grades: Pass or Fail
The final grade is based on combined exam and project assessment.
Grading criteria for the subject can be found at www.miun.se/gradingcriteria.
Grading system
The grades A, B, C, D, E, Fx and F are given on the course. On this scale the grades
A through E represent pass levels, whereas Fx and F represent fail levels.
Course reading
Required literature
Author:
Witten, Frank, Hall
Title:
Datamining - Pratical Machine Learning Tolls and Techinques
Edition:
Third edition 2011 or later
Publisher:
Elsivier
Reference literature
Author:
Ganguly et al
Title:
Knowledge discovery from sensor data
Edition:
2009 or later