Machine Learning
| Title | Μηχανική Μάθηση / Machine Learning |
| Code | DWS101 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Ioannis Vlachavas |
| Common | Yes |
| Status | Active |
| Course ID | 600015628 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) MF
Registered students: 13
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF
Registered students: 15
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
| Academic Year | 2021 – 2022 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600200251
|
Course Type 2021
Specific Foundation
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600200251
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=8135
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
Programming language Python
Learning Outcomes
Students become familiar with the main concepts as well as with specific data analysis and machine learning techniques and become also familiar with many applications of it. They also acquire skills in reviewing scientific papers, giving scientific presentations and working in practice applying various machine learning algorithms on data of different type.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction, Supervised Learning, Decision trees, Rule learning, Case based learning, Bayesian learning, Neural Networks, Support Vector Machines, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Applications in Python.
Keywords
Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Classification, Regression
Educational Material Types
- Notes
- 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
Description
Slides in electronic format, machine learning software
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 33 | ✓ | |||
| Reading Assigment | 42 | ✓ | |||
| Tutorial | 6 | ✓ | |||
| Project | 42 | ✓ | |||
| Written assigments | 39 | ✓ | |||
| self-study | 63 | ✓ | |||
| Total | 225 |
Student Assessment
Description
10% review paper and presenation of a machine leanring application area, 20% practical projects on analyzing data using software (Python), 70% written exams. These criteria are clearly stated at the course's web page.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Διδακτικές Σημειώσεις
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997.
- The Elements of Statistical Learning:Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani and J. Friedman, Springer, 2nd edition, 2009.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, 2019
- Introduction to Machine Learning. Ethem Alpaydin, The MIT Press, March 2020.
- Introduction to Machine Learning with Python: A Guide for Data Scientists, Sarah Guido and Andreas C. Müller, O' Reilly Media, 2016.
- Τεχνητή Νοημοσύνη, Δ' Έκδοση, Ι.Βλαχάβας, Π.Κεφαλάς, Ν. Βασιλειάδης, Φ.Κόκκορας και Η. Σακελλαρίου. Εκδόσεις Πανεπιστημίου Μακεδονίας, Θεσσαλονίκη 2020
- Introduction to Machine Learning (Draft of incomplete Notes), Nils J. Nilsson, 2015 (https://ai.stanford.edu/~nilsson/mlbook.html)
Last Update
18-10-2022
