Special Topics in Computer Science (MAE841)

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Department of Mathematics
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General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Undergraduate
Course Code	MAE841
Semester	8
Course Title	Special Topics in Computer Science
Independent Teaching Activities	Lectures, exercises, tutorials (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 aim of the course is to specialize in areas covered by Computer Science in applied fields. It provides background in data and information management. The specialization covers cognitive domains such as Databases, Machine Learning, Artificial Intelligence, Data Mining, etc. It also addresses all issues related to the design and optimization of computer hardware and software. This includes cognitive areas such as Programming Languages and their Implementation, Compilers, Hardware Design, Computer Architecture, Operating Systems, Distributed Systems, and more. The students of the course are expected to deepen in modern data processing techniques both theoretically and practically, while also acquiring a multifaceted knowledge of the principles of computer system design and programming. The course includes individual exercises, summary writing projects and presentation of relevant research papers. The material will be adapted and specialized according to the necessary developments and requirements.
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

The main objective of the course is to specialize in areas covered by Computer Science in applied fields such as:

Data Mining
Artificial Intelligence
Database Systems
Security of Information Systems
Distributed Systems
Mobile and Wireless Networks
Pattern Recognition
Machine Learning
Signal Processing

The specialized subject will be adapted and specialized according to the necessary developments and requirements.

Teaching and Learning Methods - Evaluation

Delivery

Lectures

Use of Information and Communications Technology

Use of projector and interactive board during lectures.

Teaching Methods

Activity	Semester Workload
Lectures	39
Working independently	78
Exercises-Homeworks	33
Course total	150

Student Performance Evaluation

Final written examination (70%)
Exercises / Homework (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:

Παπαδόπουλος, Α., Μανωλόπουλος, Ι., Τσίχλας, Κ. 20# Εισαγωγή στην Ανάκτηση Πληροφορίας, Αποθετήριο «Κάλλιπος», 2015.
Παρασκευάς, Μιχαήλ, Ειδικά θέματα εφαρμογών της Κοινωνίας της Πληροφορίας, Αποθετήριο «Κάλλιπος», 20#
Δημακόπουλος, Β. Εισαγωγή: Παράλληλα Συστήματα και Προγραμματισμός, Αποθετήριο «Κάλλιπος», 2015.