STATISTICAL LEARNING
| Title | ΣΤΑΤΙΣΤΙΚΗ ΜΑΘΗΣΗ / STATISTICAL LEARNING |
| Code | ΣΜΥ008 |
| Faculty | Sciences |
| School | Mathematics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Georgios Afendras |
| Common | Yes |
| Status | Active |
| Course ID | 600025943 |
Programme of Study: PMS Tmīmatos Mathīmatikṓn (2025-2030)
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| STATISTIKĪ, MONTELOPOIĪSĪ KAI YPOLOGISTIKES METHODOI | Elective Courses belonging to the selected specialization | Winter | - | 10 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600268400
|
Type Of Offer
- Disciplinary Course
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600268400
- eLearning:
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge on Calculus, Linear Algebra, Statistics, Probability theory.
Learning Outcomes
Upon successful completion of the course, students will:
1. understand the notions of prediction and statistical errors.
2. be able to study parametric and non-parametric models.
3. be able to compare and contrast techniques of supervised and unsupervised learning.
4. be able to measure the accuracy of a model.
5. be able to identify strong and weak aspects of various statistical learning methods.
6. understand methods for data analysis.
General Competences
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Generate new research ideas
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
This course concerns statistical inference from data. We focus on basic principles of supervised and unsupervised learning, as well as in the implementation and in applications of the models in real-world datasets. We also focus on the evaluation of the results obtained from the analysis of data. We will cover topics such as: the notion of distance in statistics. Classification methods, clustering, and dimensionality reduction. Resampling methods, cross-validation, bootstrap, support vector machines, model selection methods.
Keywords
classification, clustering, supervised learning, unsupervised learning
Educational Material Types
- Notes
- Slide presentations
- Multimedia
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
Implementation of the methods with the R programming language.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ||||
| Reading Assigment | 183 | ||||
| Tutorial | 13 | ||||
| Project | 20 | ||||
| Written assigments | 40 | ||||
| Exams | 5 | ||||
| Total | 300 |
Student Assessment
Description
The final grade for this course will be calculated as follows:
1. Midterm: 40%
2. Final exam: 60%.
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
Bibliography
Additional bibliography for study
T. Hastie, R. Tibshirani, J. Friedman, "Elements of Statistical Learning: Data mining, Inference and Prediction", 2nd edition, Springer (2009).
G. James, D. Witten, T. Hastie, R. Tibshirani, "An introduction to statistical learning: with applications in R", Springer texts in Statistics (2017).
N. Cesa-Bianchi, G. Lugosi, "Prediction, learning, and games", Cambridge university press (2006).
Kevin Patrick Murphy, "Probabilistic Machine Learning", MIT Press, 2022.
C.M. Bishop, "Pattern Recognition and Machine Learning", Springer 2006.
Shai Shalev-Shwartz and Shai Ben-David, "Understanding Machine Learning: From Theory to Algorithms", Cambridge University Press. 2014
Last Update
11-05-2025
