Seminar in Algorithmic Optimization (MAE883): Διαφορά μεταξύ των αναθεωρήσεων

Από Wiki Τμήματος Μαθηματικών
(Νέα σελίδα με '* Ελληνική Έκδοση {{Course-UnderGraduate-Top-EN}} {{Menu-OnAllPages-EN}} === General === {| class="wikitable" |- ! School | School of Science |- ! Academic Unit | Department of Mathematics |- ! Level of Studies | Undergraduate |- ! Course Code | ΜΑΕ883 |- ! Semester | 8 |- ! Course Title | Seminar in Algorithmic Optimization |- ! Independent Teaching Activitie...')
 
Χωρίς σύνοψη επεξεργασίας
 
Γραμμή 1: Γραμμή 1:
* [[Σεμινάριο Αλγοριθμικής Βελτιστοποίησης (MAE883)|Ελληνική Έκδοση]]
* [[Σεμινάριο Αλγοριθμικής Βελτιστοποίησης (ΜΑΕ883)|Ελληνική Έκδοση]]
{{Course-UnderGraduate-Top-EN}}
{{Course-UnderGraduate-Top-EN}}
{{Menu-OnAllPages-EN}}
{{Menu-OnAllPages-EN}}

Τελευταία αναθεώρηση της 19:48, 19 Αυγούστου 2024

General

School

School of Science

Academic Unit

Department of Mathematics

Level of Studies

Undergraduate

Course Code

ΜΑΕ883

Semester

8

Course Title

Seminar in Algorithmic Optimization

Independent Teaching Activities

Lectures (Weekly Hours: 3, Credits: 6)

Course Type

Special Background

Prerequisite Courses None. However it is desirable to have a strong knowledge of basic notions in Data Structures and Analysis of Algorithms.
Language of Instruction and Examinations

Greek

Is the Course Offered to Erasmus Students

No

Course Website (URL) See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes
  • The ability to write a complete report on a scientific subject.
  • The ability to present this report.
  • Working in teams.

The report can be, but not required to be, original. Further details can be determined by the teaching professor.

General Competences
  • Αnalyse and combine data and information using various technologies.
  • Working independently and in groups.
  • Free, creative, analytic, and conclusive thinking.
  • Decision making.

Further details can be determined by the teaching professor.

Syllabus

The course deals each time with a modern topic related to the area of algorithmic optimization and focuses on optimization problems with proven efficient complexity as well as on efficient methods for finding approximate solutions. In the context of the seminar, emphasis is given on the solution methods as well as the algorithmic techniques that can be exploited to find optimal solutions. Further details can be determined by the teaching professor.

Teaching and Learning Methods - Evaluation

Delivery

Details will be determined by the teaching professor.

Use of Information and Communications Technology

Details will be determined by the teaching professor.

Teaching Methods
Activity Semester Workload
Study in class 39
Self Study 75
Exercises 33
Course total 150
Student Performance Evaluation
  • There is no final exam.
  • Each student must write a report on a specific subject.
  • Each student must present the report publically.
  • Students may miss up to 3 lectures.

Other means of evaluation can be determined by the teaching professor.

Attached Bibliography

Bibliography is suggested by the teaching professor, depending on the subject under study.