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Lecture 24: Introduction Support Vector Machine, by S. Sarkar

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Lecture 27: Nonlinear SVM and Kernel Function, by S. Sarkar

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Lecture 14: Feature Extraction, by S. Sarkar

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Lecture 06 : Introduction to Decision Trees, by S. Sarkar

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Lecture 02: Different Types of Learning, by S. Sarkar

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Lecture 18: Bayesian Learning, by S. Sarkar

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Lecture 03: Hypothesis Space and Inductive Bias, by S. Sarkar

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Lecture 41 : Introduction to Clustering, by S. Sarkar

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Lecture 23 : Logistic Regression, by S. Sarkar

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Lecture 35: Tutorial VI, by S. Sarkar

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Lecture 05 : Linear Regression, by S. Sarkar

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Lecture 36: Introduction to Computational Learning Theory, by S. Sarkar

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Lecture 07 : Learning Decision Tree, by S. Sarkar

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Lecture 19: Naive Bayes, by S. Sarkar

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Lecture 20 : Bayesian Network, by S. Sarkar

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Lecture 16: Python Exercise on kNN and PCA, by S. Sarkar

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Lecture 22: Tutorial IV, by S. Sarkar

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TEM Imaging 1, by S. Sankaran

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XRD Tutorial-1, by S. Sankaran

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Probability Distributions, by S. Sankaran

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Basic Stereological Parameters Part 2, by S. Sankaran

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Factors Affecting Intensities of X-Ray Peaks, by S. Sankaran

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Lecture 20-Electron Channeling Contrast Imaging (ECCI) , Electron Back Scattered Diffraction (EBSD)-Theory & Instrument Demonstration, by S. Sankaran

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Counting of Grains and Particles Part 2, by S. Sankaran

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Lecture 15-SEM and its Mode of Operation-Continuation, Relation between Probe Current and Probe Diameter, Summary, by S. Sankaran

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Counting of Grains and Particles Part 4, by S. Sankaran

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Lecture 13 Interaction between Electrons and Sample, Imaging Capabilities, Structural Analysis, Elemental Analysis, by S. Sankaran

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Method of Stereology, by S. Sankaran

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Lecture 53-Quantitative Metallography-Tutorial 6, by S. Sankaran

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TEM Instrument Demonstration, by S. Sankaran

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