CMPT 465 - Neural Networks and Learning Systems

Description
This course provides the basic concepts of neural networks and other learning techniques including but not limited to: biological foundations of neural networks, basics of neural information processing, an artificial neuron and its activation function, multilayer feedforward neural networks and backpropagation learning, deep learning, Hopfield neural networks and associative memories, recurrent neural networks, support vector machines, validation of learning results, and clustering. Laboratory exercises provide experience with design and utilization neural and other machine learning algorithms and solving real-world classification, prediction, pattern recognition and intelligent data analysis problems. A course project will help students to develop their team-working skills and get a good experience in software project design. Cross-listed with CMPG-465 Neural Networks and Learning Systems
Credits
3
Recent Professors
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Recent Semesters
Fall 2019, Fall 2017
Offered
MTh
Avg. Class Size
20
Avg. Sections
1