Advanced Methods in Data Analysis

Year:
1st year
Semester:
S1
Programme main editor:
(I2CAT)
Onsite in:
UBB
Remote:
ECTS range:
7 ETCS

Professors

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Professors
Horia F. Pop
UBB

Prerequisites:

Algorithmics, data structures, statistics

Pedagogical objectives:

The course aims to introduce the student to advanced methods of data analysis. The course emphasizes the necessity of intelligent data analysis methods by studying some relevant practical applications.

Evaluation modalities:

The evaluation consists of:

  • Written essay,
  • Theoretical research report, and
  • Experimental research report.

The evaluation of each report is based on the evaluation of a written paper and an oral presentation.

Description:

The course presents the field of intelligent data analysis as a novel research and application domain and offers students the instruments that will allow them to develop different data analysis applications.

Topics:

  • Introduction
  • Introduction to fuzzy sets
  • Fuzzy logic, fuzzy reasoning
  • Fuzzy control systems
  • Introduction to rough sets
  • Applications of rough sets
  • Fuzzy clustering
  • Multivariate analysis
  • Feature extraction, performance analysis
  • Applications of data analysis

Required teaching material

• J. Han, M. Kamber, Data Mining: Concepts and Techniques, Academic Press, 2001. • G.J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic, Prentice Hall, 1995. • T. Mitchell, Machine Learning, McGraw Hill, 1996. • Z. Pawlak, Rough Sets, Polish Academy of Sciences, Gliwice, 2004. • N. Ye, The Handbook of Data Mining, Lawrence Elbaum Associates Publishers, 2003.

Teaching volume:
lessons:
28 hours
Exercices:
Supervised lab:
14 hours
Project:
14 hours

Devices:

  • Laboratory-Based Course Structure
  • Open-Source Software Requirements