This Learning Object is part of the Greek Master degree in “Digital Technologies and Smart Infrastructures in Agriculture” and its objective is the specialization in the scientific domain of smart technologies and infrastructures across the agri-food supply chain and the protection of the environment
For more information about the programme click here
Module details
Participants will be able to analyze data, apply basic ML techniques, and develop simple predictive models using common tools
Proposer: Agricultural University of Athens
Module designer: Department of Natural Resources Development and Agricultural Engineering
Organization: Agricultural University of Athens
Duration: 6h
ECTS: 6
Tools required:
Computer with network
Shortcut access code: No
Year of pubblication: 2026
Topics
Tags: artificial intelligence, data analysis, machine learning, neural networks, optimization
Subject areas: Communication and Cyber-physical Systems
Delivery methods: E-Learning Asynchronous
Teaching methods: Lectures
Learning objectives
- Introduce core concepts of computational intelligence and machine learning,
- Focus on data-driven modeling and algorithmic thinking
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Definitions for Artificial Intelligence, Computational Intelligence and Machine Learning
Overview of AI, computational intelligence, and machine learning concepts
Description: Focus on methods for organizing, transforming, and visualizing data prior to analysis. It highlights the importance of data quality and explores tools for identifying patterns and insights.Duration: 1hTeacher: Loukatos DimitriosDelivery method: E-Learning AsynchronousTeaching method: LecturesRequired tools: Computer with network
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Mathematical optimization techniques
Introduction to optimization methods in engineering
Description: Mathematical optimization techniques used to solve complex problems. Focus on finding optimal solutions under constraints, with applications in machine learning and engineering systemsDuration: 1hTeacher: Arvanitis Konstantinos G.Delivery method: E-Learning AsynchronousTeaching method: LecturesRequired tools: Computer with network -
Graphs and their utilization in optimization problems
Use of graph theory in optimization
Description: It explores graph-based methods for solving optimization problems, how nodes and edges can represent systems and support efficient problem-solvingDuration: 1hTeacher: Loukatos DimitriosDelivery method: E-Learning AsynchronousTeaching method: LecturesRequired tools: Computer with network -
Neural network fundamentals and applications
Introduction to neural networks and their applications
Description: It presents the basic structure and operation of neural networks, highlighting their use in pattern recognition, prediction, and intelligent system designDuration: 1hTeacher: Loukatos DimitriosDelivery method: E-Learning AsynchronousTeaching method: LecturesRequired tools: Computer with network -
Common platforms and tools for machine learning
Overview of ML tools and platforms
Description: Introduces widely used platforms and tools for machine learning development, focus on practical implementation and experimentation.Duration: 1hTeacher: Loukatos DimitriosDelivery method: E-Learning AsynchronousTeaching method: LecturesRequired tools: Computer with network -
Smart system examplification using low-cost components
Practical implementation of smart systems
Description: Presents examples of smart systems built with low-cost components, demonstrates how intelligent functionalities can be achieved affordably.Duration: 1hTeacher: Loukatos DimitriosDelivery method: E-Learning AsynchronousTeaching method: LecturesRequired tools: Computer with network