CMPG-765/CMPT-465
Neural Networks and Learning Systems

Last updated: Spring 2024

Syllabus

Lecture Notes:

Lecture 1. Introduction to Neural Networks and Learning Systems   pptx pdf

Lecture 2. McCulloch-Pitts Neuron. Hebbian Learning                  pptx pdf

Lecture 3. Linear Separability. Threshold Neuron.

      Error-Correction Learning. Perceptron.                             pptx pdf

Lecture 4. The simplest feedforward Neural Network and

      XOR problem. Hopfield Neural Network                            pptx pdf

Lecture 5. Sigmoid Activation Function.

      Feedforward Neural Network (MLP-MLF)                        pptx pdf

Lecture 6. Error Backpropagation. MLP-MLF Learning Algorithm pptx pdf

Lecture 7.   Pattern Recognition. Classification. Learning Strategies.

Validation of Learning Results                                        pptx pdf

Lecture 8. Support Vector Machine (SVM)                                           pptx pdf

Lecture 9. Complex-Valued Neurons. Multi-Valued Neuron

   and its Error-Correction Learning                                                   pptx pdf

Lecture 10. Multilayer Neural Network with

      Multi-Valued Neurons. Derivative-Free Learning              pptx pdf

Lecture 11. MLMVN with Soft Margins.

Multi-Valued Neuron with a Periodic Activation Function.                pptx pdf

Lecture 12. Time Series Prediction                                                         pptx pdf

Lecture 13. Batch Learning                                                                     pptx pdf

Lecture 14. Deep Learning. Auotoencoding.

        Convolutional Neural Networks                                          pptx pdf

Lecture 15. Unsupervised Learning. Clustering Techniques                pptx pdf