IMT4133 - Data Science for Security and Forensics


Examination arrangement

Examination arrangement: Assignment and Written examination
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Assignment 4/10
Written examination 6/10 3 hours E

Course content

-Learning, Intelligence, and Machine learning basics: principles, measures, performance evaluation, method combinations.
-Knowledge representations: discriminant and regression functions, probability distributions, Bayesian classifier.
-Learning as search: Exhaustive search, heuristic search, genetic algorithms.
-Attribute quality measures: measures for classification, measures for regression, application of feature-selection measures.
-Data preprocessing: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA).
-Supervised symbolic and statistical learning, basics of artificial neural networks.
-Unsupervised Learning and cluster analysis: hierarchical and partial clustering.
-Data classification: Bayesian classifier, k-NN classifier, multi-layered perceptron (MBPN), support vector machine (SVM), and Random Forrest.
-Data clustering: k-means clustering, Self-Organizing map (SOM).
-Classification and clustering validity testing: leave-one-out, ground truth.

Practical tasks may include:
-Realize some search methods
-Realize some classification methods
-Realize some clustering methods

Learning outcome

-Understand principles how multidimensional statistical methods differ from one dimensional methods.
-Understand the distribution of information in statistical analysis and meaning in data representation.
-Extract features from raw, measured values of data to be analyzed.
-Program some basic classification and clustering methods and test their validity.
-Program some basic Neural networks methods and test their validity.
-To apply basic statistical and data analysis methods to data relevant in information security, forensics and/or color/media technology

-The students can use relevant scientific methods in independent research and development in machine learning and pattern recognition.
-The students are capable of carrying out an independent limited research or development project in machine learning and pattern recognition under supervision, following the applicable ethical rules.

General competence:
-The students can work independently and are familiar with terminology of machine learning and pattern recognition as well as their application in the security and forensics domain.

Learning methods and activities

-Lab work
-4 major assignments that include theoretical and practical aspects of the topics (graded)

Further on evaluation

-For the written exam: Ordinary re-sit examination in August.
-The major assignments, if passed, need not be re-submitted.

Forms of assessment:
-Written exam (60%)
-4 major assignments (40% total, 10% each)
-The written exam and all major assignments must be passed

Specific conditions

Admission to a programme of study is required:
Information Security (MIS)
Information Security (MISD)

Course materials

Books/standards, conference/journal papers and web resources, such as:
-Kononenko, M. Kukar, Machine Learning and Data Mining: Introduction to Principles and Algorithms, Horwood Publishing, Chichester, U.K., 2007, ISBN 1-904275-21-4

Recommended further reading:
-T. Mitchell, Machine Learning, McGraw Hill, 1997.
-R.O.Duda, P.E. Hart, and D.G. Stork: Pattern Classification. 2nd edition., Wiley, 2001.
-S. Theodoridis, and K. Koutroumbas. Pattern Recognition, 3rd edition. Academic Press.

Credit reductions

Course code Reduction From To
IMT4612 3.7 2017-09-01
IMT4632 3.7 2017-09-01


Detailed timetable


Examination arrangement: Assignment and Written examination

Term Statuskode Evaluation form Weighting Examination aids Date Time Room *
Spring ORD Assignment 4/10
Spring ORD Written examination 6/10 E
  • * The location (room) for a written examination is published 3 days before examination date.
If more than one room is listed, you will find your room at Studentweb.