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S-R, J-K and D Flip Flops, by S. Srinivasan

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Review of Flip-Flops, by S. Srinivasan

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Xilinx Place & Route Tool, by S. Srinivasan

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Lecture 30: Introduction, by S. Sarkar

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Lecture 28: SVM : Solution to the Dual Problem, by S. Sarkar

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Lecture 25: SVM : The Dual Formulation, by S. Sarkar

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Lecture 08 : Overfitting, by S. Sarkar

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Lecture 21: Python Exercise on Naive Bayes, by S. Sarkar

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Lecture 26: SVM : Maximum Margin with Noise, by S. Sarkar

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Lecture 29: Python Exercise on SVM, by S. Sarkar

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Lecture 17: Tutorial III, by S. Sarkar

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Tutorial I, by S. Sarkar

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Lecture 32 : Neural Network and Backpropagation Algorithm, by S. Sarkar

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Lecture 01: Introduction, by S. Sarkar

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Lecture 15: Collaborative Filtering, by S. Sarkar

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Lecture 44: Python Exercise on Kmeans Clustering, by S. Sarkar

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Lecture 42 : Kmeans Clustering, by S. Sarkar

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Lecture 38: VC Dimension, by S. Sarkar

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Lecture 12: K-Nearest Neighbour, by S. Sarkar

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Lecture 33: Deep Neural Network, by S. Sarkar

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Lecture 04: Evaluation and Cross-Validation, by S. Sarkar

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Lecture 37: Sample Complexity : Finite Hypothesis Space, by S. Sarkar

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Lecture 39 : Introduction to Ensembles, by S. Sarkar

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Lecture 43: Agglomerative Hierarchical Clustering, by S. Sarkar

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Lecture 40 : Bagging and Boosting, by S. Sarkar

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Tutorial II, by S. Sarkar

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Lecture 9: Python Exercise on Decision Tree and Linear Regression, by S. Sarkar

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Lecture 13: Feature Selection, by S. Sarkar

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Lecture 34: Python Exercise on Neural Network, by S. Sarkar

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Lecture 31: Multilayer Neural Network, by S. Sarkar

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