ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY

Course Information
TitleΠΡΟΗΓΜΕΝΗ ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ ΚΑΙ ΑΝΑΚΑΛΥΨΗ ΓΝΩΣΗΣ / ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
CodeIS25
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorGrigorios Tsoumakas
CommonNo
StatusActive
Course ID40002560

Programme of Study: PPS School of Informatics (2014-today)

Registered students: 7
OrientationAttendance TypeSemesterYearECTS
TECΗNOLOGIES GNŌSĪS DEDOMENŌN KAI LOGISMIKOUElective Courses belonging to the selected specialization217.5
TECΗNOLOGIES PLĪROFORIAS KAI EPIKOINŌNIŌN STĪN EKPAIDEUSĪElective Courses217.5
PSĪFIAKA MESA- YPOLOGISTIKĪ NOĪMOSYNĪElective Courses217.5
DIKTYAKA SYSTĪMATAElective Courses217.5

Programme of Study: PPS of School of Informatics (2013-today)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
Information SystemsCompulsory217.5
Information And Communication Technologies In EducationElective Courses217.5
Digital MediaElective Courses217.5
Communication Systems and TechnologiesElective Courses217.5

Class Information
Academic Year2015 – 2016
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
600011311
Type of the Course
  • Scientific Area
Mode of Delivery
  • Face to face
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
Required Courses
  • IS02 MACHINE LEARNING
Learning Outcomes
The objectives of the course include (a) the acquisition of specialized knowledge to address issues that arise in real-world applications (class imbalance, unequal classification error costs, limited training data, large data sets, data streams, data with multiple labels, instances and relations), (b) the acquisition of useful skills for researchers and practitioners (reading, writing and evaluating scientific publications, performing comparative analysis of machine learning algorithms, using data analysis software, implementing learning algorithms), and (c) acquaintance with modern popular applications (opinion mining, data mining for the retail business). The ultimate goal is to prepare students for both a professional career and the continuation of their studies at the third cycle.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Supervised ensemble methods, cost-sensitive learning, class imbalance, comparative evaluation of learning algorithms, multi-label learning, multi-instance learning, active learning, reading, evaluating and writing scientific publications, relational data mining, learning from data streams, opinion mining, data mining in the retail business.
Keywords
machine learning, data mining, knowledge discovery
Educational Material Types
  • Slide presentations
  • scientific publications
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Description
Presentation of slides from a computer, use of software (Weka, Matlab) to demonstrate the theoretically presented techniques
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures391.3
Reading Assigment692.3
Project391.3
Written assigments391.3
Exams391.3
Total2257.5
Student Assessment
Description
Final written examination on all course material. The weight of the final exam is 40% of the final grade. Three group assignments. The weight of these assignment is 10%, 20% and 30% of the final grade respectively.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
  • Written Assignment (Summative)
  • Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Παρουσιάσεις μαθήματος, λίστα διαφορετικών συγγραμάτων ή/και επιστημονικών δημοσιεύσεων ανά αντικείμενο του μαθήματος. Ενδεικτικά: - Zhou, Z.H. (2012) Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. - Nathalie Japkowicz and Mohak Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, New York, NY, USA. - Burr Settles, Active Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning Morgan & Claypool Publishers, June 2012.
Additional bibliography for study
Επιστημονικές δημοσιεύσεις.
Last Update
04-04-2016