Special Topics in Computer Science (ΠΛ10)

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General

School School of Science
Academic Unit Department of Mathematics
Level of Studies Graduate
Course Code ΠΛ10
Semester 2
Course Title Special Topics in Computer Science
Independent Teaching Activities Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type Elective
Prerequisite Courses

641 - Design and Analysis of Algorithms

Language of Instruction and Examinations

Greek

Is the Course Offered to Erasmus Students Yes (in English)
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 - Homework 70.5
Course total 187.5
Student Performance Evaluation
  • Written exercises (50%)
  • Essay / report (20%)
  • Public presentation (30%)

Attached Bibliography

  • Evans Alan, Martin Kendall, Poatsy Mary Anne, Εισαγωγή στην πληροφορική: Θεωρία και Πράξη, Κωδικός Βιβλίου στον Εύδοξο: 41955480, 2014
  • Παπαδόπουλος, Α., Μανωλόπουλος, Ι., Τσίχλας, Κ. 2015. Εισαγωγή στην Ανάκτηση Πληροφορίας, Αποθετήριο «Κάλλιπος», 2015.
  • Παρασκευάς, Μιχαήλ, Ειδικά θέματα εφαρμογών της Κοινωνίας της Πληροφορίας, Αποθετήριο «Κάλλιπος», 2015.
  • Δημακόπουλος, Β. Εισαγωγή: Παράλληλα Συστήματα και Προγραμματισμός, Αποθετήριο «Κάλλιπος», 2015.