Special Topics in Computer Science (ΠΛ10)

Graduate Courses Outlines - Department of Mathematics

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%)