Algorithm Engineering (MAE844)

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Undergraduate Courses Outlines
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General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Undergraduate
Course Code	MAE844
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
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes	This course aims at introducing to students the concepts , techniques, properties, developments and applications of basic and advanced algorithms and data structures. 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. 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

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

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

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:

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.