MACHINE LEARNING

Course Information

TitleΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ / MACHINE LEARNING
CodeIS02
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorIoannis Vlachavas
CommonNo
StatusActive
Course ID40002247

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

Registered students: 12
OrientationAttendance TypeSemesterYearECTS
TECΗNOLOGIES GNŌSĪS DEDOMENŌN KAI LOGISMIKOUElective Courses belonging to the selected specialization117.5
TECΗNOLOGIES PLĪROFORIAS KAI EPIKOINŌNIŌN STĪN EKPAIDEUSĪElective Courses117.5
PSĪFIAKA MESA- YPOLOGISTIKĪ NOĪMOSYNĪElective Courses117.5
DIKTYAKA SYSTĪMATAElective Courses117.5

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

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
Information SystemsCompulsory117.5
Information And Communication Technologies In EducationElective Courses117.5
Digital MediaElective Courses117.5
Communication Systems and TechnologiesElective Courses117.5

Class Information

Academic Year2015 – 2016
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600011295
Type of the Course
  • Scientific Area
  • Skills Development
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Learning Outcomes
Students become familiar with the main concepts of machine learning as well as with specific machine learning techniques. They also become familiar with applications of machine learning. They acquire skills in reviewing scientific papers, giving scientific presentations and working in practice with data using relevant machine learning software.
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
Course Content (Syllabus)
Introduction, Concept, Learning, Decision Trees, Rule Learning/Inductive Logic Programming, Instance - Based Learning, Bayesian Learning, Learning with Genetic Algorithms, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Fuzzy Learning, Text Mining, Mining Biological Data, Temporal Data Mining, Machine Learning Software.
Keywords
Machine Learning, Supervised learning, Unsupervised Learning, Classification, Regression
Educational Material Types
  • Notes
  • Slide presentations
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Description
Slides in electronic format, machine learning software
Course Organization
ActivitiesWorkloadTotal hours of student effort for the semester. Includes lectures, labs, field etc.ECTSThe credit units (ECTS) of the respective teaching activity. Each unit correponds to 30 hours of student workload.IndividualFor the learning activity cooperation between students is not requisiteTeamworkFor the learning activity the students cooperate in teamsErasmusThe learning activity is available to students of exchange programmes
Lectures391.3
Reading Assigment421.4
Project421.4
Written assigments391.3
self-study632.1
Total2257.5
Student Assessment
Description
20% review paper and presenation of a machine leanring application area, 20% practical project on analyzing data using software, 60% written exams. These criteria are clearly stated at the course's web page.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (SummativeSummative assessment refers to the assessment of the learning and summarizes the development of learners at a particular time.)
  • Written Exam with Short Answer Questions (SummativeSummative assessment refers to the assessment of the learning and summarizes the development of learners at a particular time.)
  • Written Assignment (SummativeSummative assessment refers to the assessment of the learning and summarizes the development of learners at a particular time.)
Bibliography
Course Bibliography (Eudoxus)
Διδακτικές Σημειώσεις
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997 - Introduction to Machine Learning. Ethem Alpaydin, The MIT Press, October 2004. - Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Addison Wesley. 2005 - Data Mining, Practical Machine Learning Tools and Techniques with Java Implementation (second edition), Ian Witten & Eibe Frank, Morgan Kaufmann, 2005. - Introduction to Machine Learning (Draft of Incomplete Notes), Nils J. Nilsson, 2015
Last Update
20-04-2016