Προβολή πηγαίου κώδικα για τη σελίδα Algorithm Engineering (MAE844)

=== General ===
{| class="wikitable"
|-
! School
|
School of Science
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! Academic Unit
|
Department of Mathematics
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! Level of Studies
|
Undergraduate
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! Course Code
|
MAE844
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! Semester
|
8
|-
! Course Title
|
Algorithm Engineering
|-
! Independent Teaching Activities
|
Lectures, laboratory exercises, tutorials, quiz (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
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! Course Website (URL)
| -
|}
=== Learning Outcomes ===
{| class="wikitable"
|-
! Learning outcomes
|
This course aims at introducing to students the concepts , techniques, properties, developments and applications of basic and advanced algorithms and data structures. 
<br/>
Software development and software libraries that allow to easily develop and evaluate experimentally algorithms. Methodologies related to experimental research of efficient algorithms and data structures.
<br/>
After successfully passing this course the students will be able to:
* Understand basic algorithmic techniques 
* Analyze complex algorithms 
* Design and develop new algorithmic tools for experimental evaluation
|-
! General Competences
|
* Search for, analysis and synthesis of data and information, with the use of the necessary technology
* Working independently
* Team work
* Project planning and management
|}
=== Syllabus ===
* Introduction to algorithm engineering 
* Methodology of Algorithm Engineering: motivation, applications, software systems
* System checking  
* Software reliability and correctness 
* STL and Generalized programming 
* Experimental evaluation of algorithms 
=== Teaching and Learning Methods - Evaluation ===
{| class="wikitable"
|-
! Delivery
|
Lectures
|-
! Use of Information and Communications Technology
|
* Use of projector and interactive board during lectures.
* Course website maintenance. Announcements and posting of teaching material (lecture slides and notes, programs).
* Announcement of assessment marks via the ecourse platform by UOI.
|-
! Teaching Methods
|
{| class="wikitable"
! Activity
! Semester Workload
|-
| Lectures
| 39
|-
| Working independently
| 78
|-
| Team work
| 33
|-
| Course total 
| 150
|}
|-
! Student Performance Evaluation
|
* Final written examination (70%)
* Exercises (30%)
|}

=== Attached Bibliography ===
* K. Mehlhorn and S. Naeher, LEDA: A platform for combinatorial and geometric computing, Cambridge University Press, 1999.
* M. Mueller-Hannemanni and S. Schirra, Algorithm Engineering - Bridging the Gap between Algorithm Theory and Practice, Springer 2010.
* C.C. McGeoch, A Guide to Experimental Algorithmics, Cambridge University Press, 2012.
* J. Siek, L.Q. Lee, and A. Lumsdaine, The Boost Graph Library, Addison-Wesley, 2002.
* M.A. Weiss, Data structures and problem solving with C++, 2 Edition, Addison-Wesley, 2000.