Last updated: Spring 2024
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