Computational Intelligence and Machine Learning


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.

1STDefinitions for Artificial Intelligence, Computational Intelligence and Machine Learning
2NDMathematical optimization techniques (e.g., linear programming)
3RDComplexity of algorithms and problems
4THExpert systems, decision trees and applications
5THGraphs and their utilization in optimization problems
6THBio-inspired optimization algorithms
7THEvolutionary computation examples
8THFuzzy logic in artificial intelligence problems
9THNeural network  fundamentals
10TH Neural network application paradigm
11THHybrid computational intelligence techniques
12THCommon platforms and tools for machine learning
13THExperimentation with emphasis on agricultural applications

Exams, marking and student assessment

A combination of written exams and project assignments during the semester