COMPUTATIONAL METHODS FOR DECISION MAKING
Διδάσκοντας/ουσα:
Κωδικός Μαθήματος:
I/II-6
Τύπος Μαθήματος:
Υποχρεωτικό Μάθημα Κατεύθυνσης
Έτος Σπουδών:
Α'
Εξάμηνο Σπουδών:
Εαρινό
Αριθμός Πιστωτικών Μονάδων (ECTS):
6
Γλώσσα Διδασκαλίας:
English / Greek
Περιγραφή:
This course introduces students to algorithms and techniques for automated computational methods and information systems that support decision making. Emphasis is given on information processing methods that can successfully and securely execute a variety of missions in complex environments while exploiting multiple sources of sensor and open domain data. Case studies are presented, along with the lectures, in areas such as resource optimization, renewable sources of energy, financial analysis and web content personalization such as recommender systems.
Προαπαιτήσεις:
None
Περιεχόμενο του μαθήματος (Syllabus):
1. Introduction to decision making. Decision examples of engineering projects related to energy and climate.
2. Decision Support and Cumulative Cash-Flow Diagrams. Case study: “Energy saving light bulbs”.
3. Decisions Based in Engineering Economy Principles. Case Study: “Ground Heat”.
4. Regression Models. Training/Test/Validation in Data Analysis. Case Studies: Home Energy Efficiency & Home Energy Savings.
5. Computer decisions by Fuzzy Control.
6. Decisions Based on Cluster Analysis and the Fuzzy C-Means algorithm.
7. Decisions based on a model. Case Study: “Global warming”.
8. Algorithms for statistical classification. Case studies in continuous and discrete problems.
9. Computational-intelligence methods (neural networks, genetic algorithms).
10. Financial data predictions.
11. Recommender Systems.
Διδακτικές και Μαθησιακές Μέθοδοι:
In class teaching, online lectures (Moodle, Big Blue Button), case studies, decision making software hands-on, distance learning with assignments and email teacher feedback, in teams of 2-5 students. All course material is online and free of charge.
Μέθοδοι αξιολόγησης / βαθμολόγησης:
Final Exam 100%
Αντικειμενικοί Στόχοι μαθήματος (επιδιωκόμενα μαθησιακά αποτελέσματα):
- Understand the process of decision making (in a municipality for instance)
- Have a working knowledge of different decision-making tools and techniques.
- Understand various methods for decision making using classification and clustering algorithms.
- Be able to effectively apply algorithms to solve decision making problems from various problem domains, e.g., Financial Engineering.
- Be familiar with several successful applications of decision making within energy and climate.
Περίγραμμα του Μαθήματος:
