SMART LOGISTICS AND SUPPLY CHAIN MANAGEMENT AND MACHINE LEARNING
| Title | ΕΥΦΥΗΣ ΕΦΟΔΙΑΣΤΙΚΗ-ΔΙΑΧΕΙΡΙΣΗ ΕΦΟΔΙΑΣΤΙΚΗΣ ΑΛΥΣΙΔΑΣ ΚΑΙ ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ / SMART LOGISTICS AND SUPPLY CHAIN MANAGEMENT AND MACHINE LEARNING |
| Code | 10602-LSC-116 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Athanasios Tsadiras |
| Common | No |
| Status | Active |
| Course ID | 600021122 |
Programme of Study: METAPTYCΗIAKO STĪN EFODIASTIKĪ KAI DIOIKĪSĪ ALYSIDAS EFODIASMOU (2024-2025)
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 2 | 1 | 5 |
Programme of Study: METAPTYCΗIAKO STĪN EFODIASTIKĪ KAI DIOIKĪSĪ ALYSIDAS EFODIASMOU (2024- SĪMERA) MF
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | Spring | - | 5 |
Programme of Study: METAPTYCΗIAKO STĪN EFODIASTIKĪ KAI DIOIKĪSĪ ALYSIDAS EFODIASMOU (2024-sīmera)
Registered students: 16
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 5 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600287221
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600287221
- eLearning: https://elearning.auth.gr/course/view.php?id=16422
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
The objective of the module is to introduce students to the field of Machine Learning and Smart L&SCΜ that can be developed through Data Mining and Artificial Intelligence methods. Upon completion of the course, students will be familiar with how various Machine Learning methods can be applied in order to develop intelligent applications that solve various L&SCΜ problems, such as classification, clustering, cooccurrence and prediction.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
Course Content (Syllabus)
The course presents the types of problems that can be solved by applying techniques of Artificial Intelligence and Machine Learning. The Data Mining methodology for Knowledge Discovery from L&SCΜ data is presented and also the use of Decision Trees for making predictions is studied. Categorization issues are covered using linear and non-linear discriminant functions (Logistic Regression, Support Vector Machine). The solution of L&SCΜ data clustering problems with techniques such as nearest neighbors or k-mean is presented. The discovery of association rules from L&SCΜ data as well as the use of Neural Networks and Deep Learning for prediction problems in the L&SCM sector are examined. Finally, the current trends in Machine Learning are presented accompanied by their contribution in the field of Smart L&SCM.
Educational Material Types
- Notes
- Slide presentations
- Book
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
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Laboratory Work | 45 | ✓ | ✓ | ||
| Reading Assigment | 40 | ✓ | |||
| Written assigments | 23 | ✓ | |||
| Exams | 3 | ✓ | |||
| Total | 150 |
Student Assessment
Description
Written Exams
Exams in Computer Lab
Written exercise
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Additional bibliography for study
• John D. Kelleher, et al. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, 2nd Edition, the MIT Press,2020.
• Provost, F. and Fawcett T., Data Science for Business, O’Reilly, 2013.
• Nicolas Vandeput, Data Science for Supply Chain Forecasting, 2nd Edition, De Gruyte 2021.
• Ramesh Sharda, Dursun Delen, Efraim Turban. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson, 2017.
• Matt Taddy, Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions, McGraw-Hill, 2019.
• Ian H. Witten et al. Data Mining: Practical Machine Learning Tools and Techniques (Weka)- 4th Edition, Morgan Kaufmann, 2016.
Last Update
18-01-2024
