Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.

Course Curriculum

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

These video tutorials are delivered by IIT Kharagpur as part of NPTEL online courses program.

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