Advanced Topics in Machine Learning
| Title | Προχωρημένα Θέματα Μηχανικής Μάθησης / Advanced Topics in Machine Learning |
| Code | DWS205 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Grigorios Tsoumakas |
| Common | Yes |
| Status | Active |
| Course ID | 600016264 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF
Registered students: 11
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 2 | 1 | 7.5 |
| Academic Year | 2023 – 2024 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600239508
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600239508
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=6684
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Learning Outcomes
Cognitive: The student shall acquire specialized knowledge to address issues that arise in real-world applications of machine learning(class imbalance, unequal classification error costs, limited resources for acquiring training data, data with multiple labels, instances and relations, need for model and decision interpretation). In addition the student will become acquainted with real-world applications of machine learning in the industry (customer segmentation, sales forecasting, energy production and demand forecasting).
Skills: The student will acquire useful skills for machine learning researchers and practitioners (reading, writing and evaluating scientific publications, using machine learning software, implementing machine learning algorithms).
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
- Generate new research ideas
- Design and manage projects
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Cost-sensitive learning, class imbalance, multi-label learning, multi-instance learning, active learning, interpretability, reading, evaluating and writing scientific publications, relational data mining, customer segmentation, sales forecasting, energy production and demand forecasting.
Keywords
machine learning, data mining, knowledge discovery
Educational Material Types
- Slide presentations
- Video lectures
- Scientific publications
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
Presentation of slides from a computer, use of software (scikit-learn) to demonstrate the theoretically presented techniques, use of online quizes for student evaluation.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Reading Assigment | 69 | ✓ | |||
| Project | 39 | ✓ | |||
| Written assigments | 39 | ✓ | |||
| Exams | 39 | ✓ | |||
| Total | 225 |
Student Assessment
Description
Final written examination on all course material 40%. Weekly quizes 10%. Individual assignments 25%. Group project 25%.
Student Assessment methods
- Written Exam with Short Answer Questions (Summative)
- Oral Exams (Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Summative)
- Report (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Παρουσιάσεις μαθήματος, λίστα διαφορετικών συγγραμμάτων ή/και επιστημονικών δημοσιεύσεων ανά αντικείμενο του μαθήματος. Ενδεικτικά:
- Nathalie Japkowicz and Mohak Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, New York, NY, USA.
- Burr Settles, Active Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning
Morgan & Claypool Publishers, June 2012.
Additional bibliography for study
Επιστημονικές δημοσιεύσεις
Last Update
03-11-2022
