Lectures in this course:37
1 - Introduction to Artificial Neural Networks (53:50)
2 - Artificial Neuron Model and Linear Regression (58:28)
3 - Gradient Descent Algorithm (56:35)
4 - Nonlinear Activation Units and Learning Mechanisms (58:09)
5 - Learning Mechanisms-Hebbian,Competitive,Boltzmann (57:16)
6 - Associative memory (58:58)
7 - Associative Memory Model (57:16)
8 - Condition for Perfect Recall in Associative Memory (59:59)
9 - Statistical Aspects of Learning (54:08)
10 - V.C. Dimensions: Typical Examples (57:44)
11 - Importance of V.C. Dimensions Structural Risk Minimization (45:47)
12 - Single-Layer Perceptions (56:13)
13 - Unconstrained Optimization: Gauss-Newton's Method (59:17)
14 - Linear Least Squares Filters (57:58)
15 - Least Mean Squares Algorithm (52:21)
16 - Perceptron Convergence Theorem (55:29)
17 - Bayes Classifier & Perceptron: An Analogy (56:55)
18 - Bayes Classifier for Gaussian Distribution (55:51)
19 - Back Propagation Algorithm (55:35)
20 - Practical Consideration in Back Propagation Algorithm (57:09)
21 - Solution of Non-Linearly Separable Problems Using MLP (57:32)
22 - Heuristics For Back-Propagation (58:05)
23 - Multi-Class Classification Using Multi-layered Perceptrons (56:11)
24 - Radial Basis Function Networks: Cover's Theorem (56:49)
25 - Radial Basis Function Networks: Separability & Interpolation (57:24)
26 Radial Basis Function as ill-Posed Surface Reconstruction (57:58)
27 - Solution of Regularization Equation: Greens Function (55:44)
28 - Use of Greens Function in Regularization Networks (57:14)
29 - Regularization Networks and Generalized RBF (48:47)
30 - Comparison Between MLP and RBF (54:09)
31 - Learning Mechanisms in RBF (54:37)
32 - Introduction to Principal Components and Analysis (56:38)
33 - Dimensionality reduction Using PCA (54:17)
34 - Hebbian-Based Principal Component Analysis (50:22)
35 - Introduction to Self Organizing Maps (39:05)
36 - Cooperative and Adaptive Processes in SOM (52:14)
37 - Vector-Quantization Using SOM (52:02)

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