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Special Topics in Computer Science (MAE841)

Special Topics in Computer Science (MAE841)

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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) -

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

  • Evans Alan, Martin Kendall, Poatsy Mary Anne, Introduction to Computer Science: Theory and Practice, Κωδικός Βιβλίου στον Εύδοξο: 41955480, 2014.
  • 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.
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