SCHOOL

Engineering

ACADEMIC UNIT

Department of Electrical and Computer Engineering

LEVEL OF STUDIES

7

COURSE CODE

ECE_ΓΚ804

SEMESTER

8o

COURSE TITLE

DATA MINING AND MACHINE LEARNING

INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits

WEEKLY TEACHING HOURS

CREDITS

Lectures

2

2

Laboratory-Project

1

1

Recitation

2

2

Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).

5

5

COURSE TYPE

general background,
special background, specialised general knowledge, skills development

Specialised general knowledge, skills development

PREREQUISITE COURSES:

There are no prerequisite courses. It is however recommended that students have at least a basic knowledge of Data Structures, Algorithms, Statistics, and Database Management Systems.

LANGUAGE OF INSTRUCTION and EXAMINATIONS:

Greek.  Instruction may be given in English if foreign students attend the course.

IS THE COURSE OFFERED TO ERASMUS STUDENTS

Yes.

COURSE WEBSITE (URL)

https://www.ceid.upatras.gr/webpages/courses/cplusplus/dm/

Learning outcomes

The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·    Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·    Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·    Guidelines for writing Learning Outcomes

At the end of this course the student will:

1.  Have acquired basic knowledge in data mining and machine learning concepts, and understand the issues that are specific to efficient implementation of such systems.

 2. Have understood the differences between alternative conceptions of data mining, as evidenced in both research and application.

3. Have understood the basic issues behind the various algorithms used for finding. 

4. Be able to understand and characterize the kinds of patterns that can be discovered by association rule mining to describe how to extend a relational system to find patterns using association rules.

5.  Be able to compare and contrast each of the following techniques, providing examples of when each strategy is superior: decision trees, neural networks, and belief networks.

At the end of the course the student will have further developed the following skills/competences:

1.     Ability to understand the various concepts of data mining and machine learning algorithms and systems,  and how these concepts are interrelated with the performance of a software system.

2.     Ability to apply these concepts in order to design and implement efficient decision support systems.

3.     Ability to work cooperatively in order to solve problems that arise during the construction of a full-fledged working software system. 

General Competences

Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

……

Others…

…….

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Decision-making

Project planning and management

Production of free, creative and inductive thinking

Production of new research ideas

1.       Introduction

2.       Preprocessing and Data Compression

3.       Classification Algorithms

4.       Clustering Algorithms

5.       Association rule discovery algorithms

6.       Bayesian networks, neural networks.

7.       Web Mining

8.       Spatial Data Mining

9.       Temporal Data Mining

10.    Data Mining from sequences

DELIVERY
Face-to-face, Distance learning, etc.

Face-to-face

USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Use of ICT in teaching, laboratory education, communication with students

We use  Information and Communications Technology in communication with students. We use e_class, e_mail, forum. The course has a web site.

TEACHING METHODS

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

The student's study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

STUDENT PERFORMANCE EVALUATION

Description of the evaluation procedure

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

(1)   Written examination (70% of the final grade).

(2)   Project (30% of the final grade)

1.           Mohammed J. Zaki, Wangner Meira Jr., Εξόρυξη και Ανάλυση Δεδομένων: Bασικές Έννοιες και Αλγόριθμοι, 2017.

2.   Tan, Steinbach, Kumar, Introduction to Data Mining, Addison-Wesley, 2007.

3.   M. Βαζιργιάννης, Μ. Χαλκίδη, Εξόρυξη Γνώσης από Βάσεις Δεδομένων, Τυπωθήτω, Δαρδάνος,2003.

4.   Margaret Dunham, Data Mining Introductory and Advanced Topics, Pearson Education, 2003.

5.   Α. Νανόπουλος, Γ. Μανωλόπουλος, Εισαγωγή στην Εξόρυξη Δεδομένων και στις Αποθήκες Δεδομένων, Εκδόσεις Νέων Τεχνολογιών, 2008. 

6.   T. M. Mitchell, Machine Learning, McGraw Hill, 1997.

7.   I.H. Witten, E. Frank, Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, October, 1999.

8.   Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd Edition, 2006.

9.   David J. Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining, MIT Press, 2000.

10.         S. Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data, Morgan-Kaufmann Publishers 2003.