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Introduction to Intelligent Systems: Foundations of AI and Practical Applications

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This optional lecture series, designed for 3rd-year PhD students, provides a comprehensive introduction to intelligent systems. Over 14 lectures and labs, students will explore essential topics including expert systems, multi-agent systems, natural-inspired systems, robotics, and machine learning. The course combines theoretical understanding with practical lab projects, allowing students to engage in hands-on learning. Students are encouraged to come in the first two weeks to select lab projects for extra credit opportunities. Written exams will assess their understanding of the course content.

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Introduction to Intelligent Systems: Foundations of AI and Practical Applications

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  1. Intelligent SystemsLecture 1 Ph. D. Lect. HoriaPopaAndreescu 2012-2013 3rd year, semester 6

  2. General description • Optional lecture • 1 semester • 14 lectures (placed in 10 weeks, I have yet to figure out how) • 14 labs of 1 hour / semigroup kept as 7 labs of 2 hours once every 2 weeks (same observation as for the lecture) • Written exam • 2 Lab projects • Extra credits opportunities • The students who want to take some projects for lab presences have to come in the first two weeks to be assigned to the projects and discuss what they have to do. Artificial Intelligence, lecture 1

  3. Bibliography • [GR] J. Giarratano, G. Riley - Expert Systems: Principles and Programming, PWS Pbs. Comp., ITP, 4th edition, 2005 • [BCG] F.L. Bellifemine, G. Caire, D. Greenwood – Developing Multi-Agent Systems with JADE, Wiley, 2007 • [W1] Mark Watson – Practical Artificial Intelligence Programming With Java AI 3rd ed., 2008 • [C] D. Cârstoiu – Sisteme Expert, Editura ALL, București, 1994 • [M] A. Martinoli - Distributed intelligent systems and algorithms laboratory DISAL http://jahia-prod.epfl.ch/page-32632-en.html Artificial Intelligence, lecture 1

  4. Intelligent systems • What can be included in a course about Intelligent Systems: • Expert systems • Multi agent systems • Natural inspired based systems: • Swarm intelligence, Ant colony optimisation, • Genetic algorithms • Robotics: • Machine learning • Collective movements • Distributed sensing and action Artificial Intelligence, lecture 1

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