Computational Intelligence and Machine Learning

Description

These lecture series are dedicated in communicating the fundamentals of Computational Intelligence and Machine Learning, with emphasis on their agricultural exploitation.
The areas being covered include but are not limited to:
Definitions for artificial intelligence, computational intelligence and machine learning. Mathematical optimization techniques. Complexity of algorithms and problems. Expert systems, decision trees and applications. Graphs and their utilization in optimization problems. Bio-inspired optimization algorithms and examples. Fuzzy logic in artificial intelligence. Neural network fundamentals and applications. Hybrid computational intelligence techniques. Common platforms and tools for machine learning. Exemplification with actual systems tailored for agricultural purposes.

WEEK	Lectures
1^ST	Definitions for Artificial Intelligence, Computational Intelligence and Machine Learning
2^ND	Mathematical optimization techniques (e.g., linear programming)
3^RD	Complexity of algorithms and problems
4^TH	Expert systems, decision trees and applications
5^TH	Graphs and their utilization in optimization problems
6^TH	Bio-inspired optimization algorithms
7^TH	Evolutionary computation examples
8^TH	Fuzzy logic in artificial intelligence problems
9^TH	Neural network fundamentals
10^TH	Neural network application paradigm
11^TH	Hybrid computational intelligence techniques
12^TH	Common platforms and tools for machine learning
13^TH	Experimentation with emphasis on agricultural applications

Exams, marking and student assessment

A combination of written exams and project assignments during the semester