Computational Intelligence- Statistical Learning

Course Information
TitleΥπολογιστική Νοημοσύνη- Στατιστική Μάθηση / Computational Intelligence- Statistical Learning
CodeAI105
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorAnastasios Tefas
CommonNo
StatusActive
Course ID600016301

Programme of Study: PMS TECΗNĪTĪ NOĪMOSYNĪ (2018 éōs sīmera) MF

Registered students: 5
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5

Programme of Study: PMS TECΗNĪTĪ NOĪMOSYNĪ (2018 éōs sīmera) PF

Registered students: 16
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5

Class Information
Academic Year2021 – 2022
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600200256
Course Type 2016-2020
  • Background
  • General Knowledge
  • Scientific Area
  • Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
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
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Educational Material Types
  • Notes
  • Slide presentations
  • Video lectures
  • Multimedia
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39
Reading Assigment
Tutorial
Project
Written assigments
Total39
Student Assessment
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Summative)
  • Oral Exams (Formative, Summative)
  • Performance / Staging (Summative)
  • Written Exam with Problem Solving (Summative)
  • Report (Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Richard O. Duda, Peter E. Hart, David G. Stork, "Pattern Classification (2nd Edition)", John Wiley & Sons, 2000 2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2008. 3. Διαφάνειες και υλικό σε ηλεκτρονική μορφή 4. E-books στα Αγγλικά
Additional bibliography for study
Sergios Theodoridis, Konstantinos Koutroumbas, "Pattern Recognition", Academic Press, 1999.
Last Update
04-10-2013