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This Repository contains Laboratory Exercises related to the "Artificial Intelligence" course.

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Artificial Intelligence - Laboratory Exercises

Overview

This repository contains the completed laboratory exercises for the Artificial Intelligence course, part of the curriculum at the Faculty of Electrical Engineering, University of Sarajevo.

The exercises cover a range of fundamental and advanced AI concepts, providing hands-on experience with various algorithms, techniques, and libraries.

Core Topics & Technologies Explored

Throughout the course, practical work included:

  • Developing and training various Neural Network architectures (including Feedforward and onvolutional Neural Networks - CNNs) using diverse datasets such as textual data and image data.
  • Implementing Expert Systems leveraging the PyKE (Python Knowledge Engine) library for both forward and backward chaining, and incorporating interactive question bases.
  • Applying Evolutionary Computation techniques, specifically Genetic Algorithms (using pyeasyga) for optimization tasks.
  • Utilizing Swarm Intelligence algorithms, focusing on Particle Swarm Optimization (PSO) (with PySwarms) for problem-solving, including neural network parameter optimization.
  • Exploring concepts of Fuzzy Logic.

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This Repository contains Laboratory Exercises related to the "Artificial Intelligence" course.

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