CourseSpecifications
Valid as from the academic year 2024-2025

Data Mining (C002703)

Course size
Credits 3.0(nominal values; actual values may depend on programme)
Study time 80 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 1)
EnglishGent

Lecturers in academic year 2024-2025
Saeys, Yvan

lectureseminar

WE02

Offered in the following programmes in 2024-2025
Master of Science in Biochemistry and Biotechnology
Exchange programme in Biochemistry and Biotechnology (master's level)

lecturer-in-charge
crdts33offeringAA

Teaching languages
EnglishKeywords
Bioinformatics, Data mining, Data analysis, Pattern recognition, Classification, Regression,Feature selection, Sequence and microarray data
Position of the course
This course provides the student with the basic principles of data mining, and its applications inbioinformatics. The analysis of high-diemnsional and complex data sets is a problem that
becomes more and more persistent in biotechnology and biology, requiring the use ofadvanced computer methods to analyze these data sets, and get insight into the processes thatare being modelled.
The course discusses both classification and regression methods, acquinting the students withthese methods in practical sessions where they can apply the methods that were seen in thetheory, using existing data mining software, and applying the techniques to real world datasets.
This course contributes to the following program competencies: Ma.WE.BB.1.2, Ma.WE.BB.
1.3,Ma.WE.BB.2.5, Ma.WE.BB.2.6
Contents• Overview of data mining techniques• • Design cycle of data mining algorithms• • Relations between bias, variance and model complexity• Classification Methods:• • Nearest Neighbors methods• • Classification Trees• • Bayesian classifiers• • Linear Discriminant Analysis• • Kernel methods (including SVM)
• • Applications in Bioinformatics• Clustering Methods:• • Hierarchical clustering• • K-means• • Self-organizing maps
• • Applications in Bioinformatics• Regression methods:• • Regression Trees• • Principal Component Regression and Partial Least Squares(Approved)1• • Other regression methods (?)• Model building, selection and inference:• • Methods to estimate prediction error• • Cross-validation• • Bootstrap• • Ensembles• • Bagging - Boosting• Methods for dealing with high dimensional data:• • Principal Component Analysis• • Independent Component Analysis• • Feature selection
Initial competences
Basic knowledge in bioinformatics, computer science and statistics
Final competences
1 The student is able to propose the appropriate method for a giving data mining problem to
1 realize a specific objective.
2 The student is able to understand, assimilate, and apply recent literature on data mining in
1 bioinformatics.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Group work, Seminar, Lecture
Extra information on the teaching methods
Theory: software demonstrations
Exercises: computer exercises, implementation as well as use of existing software packages,
project work in small groups
Study material
Type: Handouts
Name: Powerpoint presentations of the theory classes, supporting material, program code and data'
Indicative price: € 10
Optional: no
Additional information: available on Ufora
References
T. Mitchell (1997). Machine Learning. McGraw-HillR.O. Duda, P.E. Hart, and D.G. Stork. (2001) Pattern Classification, Wiley, New York
P. Baldi and S. Brunak (1998) Bioinformatics, the machine learning approach, MIT Press
Course content-related study coaching
The lecturer announces office hours for problems related to the theory.
Supervised practical sessions.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral assessment
Examination methods in case of periodic assessment during the second examination period
Oral assessment
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Extra information on the examination methods
Periodical evaluation: oral presentation of the project work and oral examination(Approved)2

Calculation of the examination mark
Periodical evaluation (theory) (50%) + non-periodical evaluation (exercises) (50%) In case a
student has not passed the non-periodical evaluation, a second chance is offered by means ofa compensatory activity between the first and the second examination period.

(Approved)3