Computational Intelligence- Statistical Learning

Course Information
TitleΥπολογιστική Νοημοσύνη- Στατιστική Μάθηση / Computational Intelligence- Statistical Learning
CodeDMCI102
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorAnastasios Tefas
CommonYes
StatusActive
Course ID600016139

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 eōs sīmera) MF

Registered students: 9
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF

Registered students: 13
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5

Class Information
Academic Year2020 – 2021
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600177049
Course Type 2021
Specialization / Direction
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)
Prerequisites
General Prerequisites
Knowledge of linear algebra, pattern recognition and machine learning, deep learning and neural networks will help the students follow easier the course schedule.
Learning Outcomes
Cognitive: Foundation and mathematical formalization of the learning and generalization concept and its importance in the computational intelligence field. Comparative conception of the basic machine learning methods. Training principles and learning abilities of the learning machines. Exposition to the diverse applications of learning machines in the fields of pattern recognition, function approximation, data mining and information retrieval. Skills: Promoting analytic and implementation skills in pattern recognition and function approximation using stastical learnin methods and implementation of the corresponding algorithms. Use of statistical learning libraries for real problems. Promoting programming skills. Ability to complete a learning problem by implementing several statistical machine learning algorithms, running experiments on real data, writing a short paper and presenting the results.
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
Course Content (Syllabus)
Non - parametric techniques in pattern recognition. Bayesian Learning, Neural networks algorithm for analyzing patterns. Statistical machine learning theory. Vapnik - Chervonenkis dimension. Support vector machines. Kernel - based learning. Multidimensional scaling. Nonlinear data - manifold learning and dimensionality reduction. Discriminant Analysis. Clustering and dimensionality reduction using graph embedding and Spectral techniques. Information theory paradigms. Fuzzy sets and fuzzy pattern recognition. Genetic and Evolutionary programming with applications in pattern recognition. Hybrid computational intelligence systems in signal, image and video analysis.
Keywords
Computational Intelligence, Statistical Machine Learning
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
Description
Slides, demos, software libraries, communication through web and mail.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39
Reading Assigment86
Project60
Written assigments15
Exams25
Total225
Student Assessment
Description
Project evaluation (source code, application, paper, presentation) 60%, written exams 40%. The evaluation criteria are explained in detail with the project anouncement. The evaluation criteria for the exams are discussed in the last lectures of the course.
Student Assessment methods
  • Written Exam with Short Answer Questions (Summative)
  • Written Exam with Extended Answer Questions (Summative)
  • Written Assignment (Formative, Summative)
  • Oral Exams (Formative, Summative)
  • Performance / Staging (Formative, 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. Free E-books στα Αγγλικά
Additional bibliography for study
Sergios Theodoridis, Konstantinos Koutroumbas, "Pattern Recognition", Academic Press, 1999.
Last Update
10-01-2023