CS 3400 - Neural Networks and Intelligent Machines

3 unit(s)

(Same as COGS 3400) The course, with its associated laboratory, deals with the current designs of cognitive machines that exploit the kind of adaptive parallel processing and self-organizing networks used by brains for learning, memory, visual imaging, and pattern recognition. The laboratory will emphasize the exploration of cognitive models using computer simulation.

Prerequisites: CS 2500 or COGS 2300 or consent of instructor.

Hours: (Lecture, 2 hours; laboratory, 3 hours)

Course Learning Outcomes List
Students will:

Identify and explain elements and functions of biological neurons and neural structures (CS PLO 1 and CogS PLO 2);
Briefly describe classical models of neural functioning, such as Hodgkins/Huxley and perceptrons (CS PLO 1 and CogS PLO 2);
Explain Multi-layer feed-forward nets with back propagation of errors, and describe typical uses (CS PLO 4 and CogS PLO 2);
Describe and explain unsupervised learning, such as Kohonen networks and describe typical uses (CS PLO 4 and CogS PLO 2);
Explain elements of machine learning (CS PLO 4 and CogS PLO 2);
Explain contemporary approaches such as deep learning and convolutional models, and build small example models using computer tools and implementations (CS PLO 4 and CogS PLO 2);
Describe and briefly explain alternative biologically inspired approaches such as genetic algorithms and ”artificial life” (CS PLO 1 and CS PLO 4 and CogS PLO 1 and CogS PLO 2); and,
Explore and discuss social, philosophical, and other issues and concerns related to the development of “artificial intelligence” (CS PLO 4 and CogS PLO 1 and CogS PLO 2).

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