Course Instructor: 

Syllabus:

• Connectionist paradigm.
• Rules and learning algorithms of neural networks with feed forward.
• Universal function approximators feed forward propagation multilayer networks.
• Recursive Hopfield Networks.
• Boltzmann Machines.
• Self-organization principle of and supervised learning.
• Broomhead & Lowe networks with radial or elliptical basis functions.
• Cascor Neural Networks.
• Extracting knowledge from neural networks.
• Evolutionary intelligent agents to implement neural networks.
• Social learning