Statistical Learning and knowledge processing
| Title | ΣΤΑΤΙΣΤΙΚΗ ΜΑΘΗΣΗΣ ΚΑΙ ΕΠΕΞΕΡΓΑΣΙΑ ΓΝΩΣΗΣ / Statistical Learning and knowledge processing |
| Code | 0531 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter |
| Common | No |
| Status | Inactive |
| Course ID | 600016211 |
Programme of Study: UPS of School of Mathematics (2014-today)
Registered students: 63
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses belonging to the selected specialization | Winter | - | 5.5 |
| Academic Year | 2019 – 2020 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 4 |
| Class ID | 600147647
|
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600147647
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=7950
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
Students will be able to solve statistical learning problems and evaluate the results by:
1. Regression or regression models
2. Classification and regression trees
3. Support Vector Machines
4. Neural networks
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in teams
- Work in an interdisciplinary team
Educational Material Types
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.3 | |||
| Laboratory Work | 40 | 1.3 | |||
| Reading Assigment | 83 | 2.8 | |||
| Exams | 3 | 0.1 | |||
| Total | 165 | 5.5 |
Student Assessment
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Oral Exams (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Labortatory Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Η Επιστήμη των Δεδομένων
Κωδικός Βιβλίου στον Εύδοξο: 77120638
Έκδοση: 1η/2018
Συγγραφείς: Β, Βερύκιος, Σ. Κωτσιαντής, Η. Σταυρόπουλος, Μ. Τζαγκαράκης
ISBN: 978-960-578-043-2
2. Η επιστήμη των δεδομένων μέσα από τη γλώσσα R,
Συγγραφέας: Βασίλειος Βερύκιος, Βασίλειος Καγκλής, Ηλίας Σταυρόπουλος
Έκδοση ebook: ΣΕΑΒ, ΚΑΛΛΙΠΟΣ (πηγή)
Έτος έκδοσης: 2015
ISBN: 978-960-603-394-0
Συγγραφέας: Βασίλειος Βερύκιος, Βασίλειος Καγκλής, Ηλίας Σταυρόπουλος
Έκδοση ebook: ΣΕΑΒ, ΚΑΛΛΙΠΟΣ (πηγή)
Έτος έκδοσης: 2015
ISBN: 978-960-603-394-0
Εφαρμοσμένη Στατιστική, Ε. Μπόρα-Σέντα, Π. Μωυσιάδης, Ζήτη, 1990
Additional bibliography for study
Η επιστήμη των δεδομένων μέσα από τη γλώσσα R,
1. An Introduction to Statistical Learning [electronic resource], Τύπος: Ηλεκτρονικό Βιβλίο, Τόμος: 103, Gareth James / Daniela Witten / Trevor Hastie / Robert Tibshirani, 2013, Springer New York, ISBN: 9781461471387
2. The Elements of Statistical Learning [electronic resource], Τύπος: Ηλεκτρονικό Βιβλίο, Τόμος: , Trevor Hastie / Robert Tibshirani / Jerome Friedman, 2009, Springer New York, ISBN: 9780387848587
Last Update
15-03-2020
