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Course Description

The theory of a novel class of information-processing systems, called cellular neural networks, which are capable of high-speed parallel signal processing, was presented in a previous paper

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Course Syllabus
  • Lec-1 Introduction to Artificial Neural Networks
  • Lec-2 Artificial Neuron Model and Linear Regression
  • Lec-3 Gradient Descent Algorithm
  • Lec-4 Nonlinear Activation Units and Learning Mechanisms
  • Lec-5 Learning Mechanisms-Hebbian,Competitive,Boltzmann
  • Lec-6 Associative memory
  • Lec-7 Associative Memory Model
  • Lec-8 Condition for Perfect Recall in Associative Memory
  • Lec-9 Statistical Aspects of Learning
  • Lec-10 V.C. Dimensions: Typical Examples
  • Lec-11 Importance of V.C. Dimensions Structural Risk Minimization
  • Lec-12 Single-Layer Perceptions
  • Lec-13 Unconstrained Optimization: Gauss-Newtons Method
  • Lec-14 Linear Least Squares Filters
  • Lec-15 Least Mean Squares Algorithm
  • Lec-16 Perceptron Convergence Theorem
  • Lec-17 Bayes Classifier&Perceptron: An Analogy
  • Lec-18 Bayes Classifier for Gaussian Distribution
  • Lec-19 Back Propagation Algorithm
  • Lec-20 Practical Consideration in Back Propagation Algorithm
  • Lec-21 Solution of Non-Linearly Separable Problems Using MLP
  • Lec-22 Heuristics For Back-Propagatio
  • Lec-23 Multi-Class Classification Using Multi-layered Perceptrons
  • Lec-24 Radial Basis Function Networks: Cover's Theorem
  • Lec-25 Radial Basis Function Networks: Separability&Interpolation
  • Lec-26 Radial Basis Function as ill-Posed Surface Reconstruc
  • Lec-27 Solution of Regularization Equation: Greens Function
  • Lec-28 Use of Greens Function in Regularization Networks
  • Lec-29 Regularization Networks and Generalized RBF
  • Lec-30 Comparison Between MLP and RBF
  • Lec-31 Learning Mechanisms in RBF
  • Lec-32 Introduction to Principal Components and Analysis
  • Lec-33 Dimensionality reduction Using PCA
  • Lec-34 Hebbian-Based Principal Component Analysis
  • Lec-35 Introduction to Self Organizing Maps
  • Lec-36 Cooperative and Adaptive Processes in SOM
  • Lec-37 Vector-Quantization Using SOM

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