Course details

(1) General
FacultyEngineering
DepartmentElectrical and Computer Engineering
Education levelPostgraduate / Master of Science
Course codeE4Semester2
Course titleData Mining
Independent teaching activitiesHours per weekECTS
Lectures2
Practice3
Total54
CoursetypeGeneral setting course, skills development
Prerequisite courses

  • Basic knowledge of Probabilistic Theory and Statistics

  • Basic knowledge of Data Structures

Teaching and assessment languageEnglish
Course URLhttps://elearning.auth.gr/course/view.php?id=12504

(2) Learning outcomes

Objective

The course aims to introduce students to the knowledge discovery process with a focus on data mining/ machine learning techniques. Within the context of the course the following concepts will be discussed and elaborated: data mining principles (data mining), supervised machine learning techniques (prediction and characterization – classification), unsupervised machine learning techniques (grouping and aggregation – clustering), anomaly detection (outlier analysis).

Knowledge and Capacities

Upon successfully completing this course, students will be familiar with:

  • Know the basic principles of data mining theory and the main application domains
  • Understand the fundamental data mining techniques
  • Apply well-known machine learning algorithms to health-related pilot problems
  • Assess the performance of algorithms on a given problem and given requirements
  • Formulate and answer data mining hypotheses

Capacities

The course participants upon completion will be able to:

  • Query big data infrastructures
  • Visualize and (pre)process big data collections
  • Understand the fundamental machine learning techniques and algorithms
  • Apply machine learning techniques (classification, clustering, regression analysis, outlier/deviation detection) to pilot problems
  • Select the most efficient algorithm, based on problem requirements

Design the methodology for big data analysis problems of medium complexity


(3) Course contents

  1. 1. Introduction to Data Mining/Machine Learning:
    – Definitions Examples Application areas
    – Data Exploration
    2. Data Preprocessing
    3. Data mining/machine learning techniques (Part I): Classification
    – Overview
    – Definitions
    – Algorithms
    4. Data mining/machine learning techniques (Part II): Clustering
    – Overview
    – Definitions
    – Algorithms
    5. Advanced Data Mining Topics:
    – Dimensionality reduction
    – Ensemble methods
    – Outlier Analysis

(4) Teaching and learning methods – evaluation

Teaching methodsFace to face
Distance learning
Use of information and
communication technologies (ICT)

  • Use of ICT in Teaching- Moodle Virtual learning environment (VLE)
    (asynchronous learning, wikis, Online Discussion Fora, Educational Portfolio, assignment submission, assessment process)

  • Use of ICT in Communication with students
    (email, instant messaging via Moodle)

Module structureWork Hours per SemesterActivity
Lectures 30
Exercises (Quiz) 15
Exercises (Online discussion fora) 15
Exercises (Study relevant papers)10
Quiz preparatory work60
Overall work for the course130
Assessment Methods
    The grade for this module is a weighted average based on:
    1. An assessment on two online quizzes (Classification/preprocessing - 40%, Clustering/advanced data mining - 50%)
    2. A final online exam (all learning material included- 90%)
    3. Participation at the course forum (10%)
    Participation in 1. is obligatory, while participation in 2. is optional.
    The final grade is the maximum between 1. and 2., plus 3.

(5) Recommended Bibliography

-Recommended Bibliography:

  1. Practical Machine Learning in R, K. Chatzidimitriou et al., Leanpub publising, 2017.
  2. Introduction to Data mining, P. Tan, M. Steinbach & V. Kumar, Addison Wesley, 2005.
  3. Data Mining; Concepts and Techniques, 2nd edition, J. Han and M. Kamber, Morgan Kaufmann, 2006.