Wiki Τμήματος Μαθηματικών
Special Topics in Computer Science (ΠΛ10): Διαφορά μεταξύ των αναθεωρήσεων

Special Topics in Computer Science (ΠΛ10): Διαφορά μεταξύ των αναθεωρήσεων

Από Wiki Τμήματος Μαθηματικών
(Νέα σελίδα με 'Graduate Courses Outlines - [https://math.uoi.gr Department of Mathematics] === General === {| class="wikitable" |- ! School | School of Science |- ! Academic Unit | Department of Mathematics |- ! Level of Studies | Graduate |- ! Course Code | ΧΧΧ |- ! Semester | 000 |- ! Course Title | ΧΧΧ |- ! Independent Teaching Activities | Lectures (Weekly Teaching Hours: 3, Credits: 7.5) |- ! Course Type | General Background |- ! Prerequisite Courses | - |- ! L...')
 
Χωρίς σύνοψη επεξεργασίας
 
(8 ενδιάμεσες αναθεωρήσεις από τον ίδιο χρήστη δεν εμφανίζεται)
Γραμμή 1: Γραμμή 1:
[[Graduate Courses Outlines]] - [https://math.uoi.gr  Department of Mathematics]
* [[Ειδικά Θέματα Πληροφορικής (ΠΛ10)|Ελληνική Έκδοση]]
{{Course-Graduate-Top-EN}}
{{Menu-OnAllPages-EN}}


=== General ===
=== General ===
Γραμμή 15: Γραμμή 17:
|-
|-
! Course Code
! Course Code
| ΧΧΧ
| ΠΛ10
|-
|-
! Semester
! Semester
| 000
| 2
|-
|-
! Course Title
! Course Title
| ΧΧΧ
| Special Topics in Computer Science
|-
|-
! Independent Teaching Activities
! Independent Teaching Activities
Γραμμή 27: Γραμμή 29:
|-
|-
! Course Type
! Course Type
| General Background
| Elective
|-
|-
! Prerequisite Courses
! Prerequisite Courses
| -
|
641 - Design and Analysis of Algorithms
|-
|-
! Language of Instruction and Examinations
! Language of Instruction and Examinations
|
|
ΧΧΧ
Greek
|-
|-
! Is the Course Offered to Erasmus Students
! Is the Course Offered to Erasmus Students
| Yes
| Yes (in English)
|-
|-
! Course Website (URL)
! Course Website (URL)
Γραμμή 49: Γραμμή 52:
! Learning outcomes
! Learning outcomes
|
|
ΧΧΧ
* 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
! General Competences
|
|
ΧΧΧ
* 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 ===
=== Syllabus ===


ΧΧΧ
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 ===
=== Teaching and Learning Methods - Evaluation ===
Γραμμή 66: Γραμμή 85:
! Delivery
! Delivery
|
|
ΧΧΧ
Lectures
|-
|-
! Use of Information and Communications Technology
! Use of Information and Communications Technology
|
|
ΧΧΧ
Use of projector and interactive board during lectures.
|-
|-
! Teaching Methods
! Teaching Methods
Γραμμή 81: Γραμμή 100:
| 39
| 39
|-
|-
| ΧΧΧ
| Working independently
| 000
| 78
|-
|-
| ΧΧΧ
| Exercises - Homework
| 000
| 70.5
|-
|-
| Course total  
| Course total  
Γραμμή 93: Γραμμή 112:
! Student Performance Evaluation
! Student Performance Evaluation
|
|
ΧΧΧ
* Written exercises (50%)
* Essay / report (20%)
* Public presentation (30%)
|}
|}


Γραμμή 99: Γραμμή 120:


<!-- In order to edit the bibliography, visit the webpage -->
<!-- In order to edit the bibliography, visit the webpage -->
<!-- https://wiki.math.uoi.gr/index.php/%CE%A0%CF%81%CF%8C%CF%84%CF%85%CF%80%CE%BF:MAM199-Biblio -->
<!-- https://wiki.math.uoi.gr/index.php/%CE%A0%CF%81%CF%8C%CF%84%CF%85%CF%80%CE%BF:MAM174-Biblio -->


{{MAM199-Biblio}}
{{MAM174-Biblio}}

Τελευταία αναθεώρηση της 05:17, 16 Ιουνίου 2023

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.
Ανακτήθηκε από «https://wiki.math.uoi.gr/index.php?title=Special_Topics_in_Computer_Science_(ΠΛ10)&oldid=4718»