Linear Algebra Applications (MAE629)

Από Wiki Τμήματος Μαθηματικών
Αναθεώρηση ως προς 18:32, 1 Μαρτίου 2024 από τον Mathwikiadmin (συζήτηση | συνεισφορές) (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 | MAE629 |- ! Semester | 6 |- ! Course Title | Linear Algebra Applications |- ! Independent Teaching Activities | Lectures, laboratory exerci...')
(διαφορά) ← Παλαιότερη αναθεώρηση | Τελευταία αναθεώρηση (διαφορά) | Νεότερη αναθεώρηση → (διαφορά)

General

School

School of Science

Academic Unit

Department of Mathematics

Level of Studies

Undergraduate

Course Code

MAE629

Semester

6

Course Title

Linear Algebra Applications

Independent Teaching Activities

Lectures, laboratory exercises (Weekly Teaching Hours: 3, Credits: 6)

Course Type

Special Background

Prerequisite Courses -
Language of Instruction and Examinations

Greek

Is the Course Offered to Erasmus Students

Yes

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

Learning Outcomes

Learning outcomes

The goal of the course is the study of applications of linear algebra to different sciences such as Economics, Sociology, Genetics, Physics. Using basic linear algebraic and probabilistic tools we create models which provide good estimates to real life problems related to the previously mentioned sciences. Among others we study the assignment model, applications to graph theory, Markov models, Game theory, Leontief model, Leslie model, Simplex method, applications to genetic, the least square method.

General Competences

Studying and analysing particular models applying to real life problems the student is expected to master the skill of applying abstract mathematical tools. Independent and teamwork. Working in an interdisciplinary.

Syllabus

  • Mathematical models
  • The assignment model
  • Applications in graph theory
  • Recursive methods
  • Markov model
  • Introduction to game theory
  • Economical models
  • Leslie model
  • Applications to genetic
  • Simplex method
  • Least square method

Teaching and Learning Methods - Evaluation

Delivery

Classroom (face-to-face)

Use of Information and Communications Technology -
Teaching Methods
Activity Semester Workload
Lectures (13X3) 39
Exam 40
Exercises-Homeworks 41
Project 30
Course total 150
Student Performance Evaluation

Written Examination, Oral Presentation, written assignments in Greek (in case of Erasmus students in English) which includes resolving application problems.

Attached Bibliography

See the official Eudoxus site or the local repository of Eudoxus lists per academic year, which is maintained by the Department of Mathematics. Books and other resources, not provided by Eudoxus: