Lec 5: Bayesian Decision Theory

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Lec 6: Bayesian Decision Theory

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Lec 7: Bayes Decision Theory - Binary Features

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Lec 8: Bayesian Decision Theory

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Lec 9: Bayesian Decision Theory

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Lec 10: Bayesian Belief Networks

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Lec 11: Parameter Estimation and Maximum Likelihood Estimation

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Lec 12: Parameter Estimation and Bayesian Estimation

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Lec 13: Concept of non-parametric techniques

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Lec 14: Density Estimation by Parzen Window

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Lec 15: Parzen Window and K nearest neighbor algorithm

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Lec 16: Linear Discriminant Functions and Perceptron Criteria (Part I)

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Lec 17: Linear Discriminant Functions and Perceptron Criteria (Part II)

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Lec 18: Linear Discriminant Functions and Perceptron Criteria (Part III)

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Lec 19: Support Vector Machine (Part I)

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Lec 20: Support Vector Machine (Part II)

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Lec 21: Logistic Regression

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Lec 22: Decision Tree

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Lec 23: Hidden Markov Model (HMM)

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Lec 24: Ensemble Classifiers (Part I)

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Lec 25: Ensemble Classifiers (Part II)

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Lec 26: Dimensionality Problem and Principal Component Analysis

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Lec 27: Principal Component Analysis

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Lec 28: Linear Discriminant Analysis (LDA) (Part I)

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Lec 29: Linear Discriminant Analysis (LDA) (Part II)

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Lec 30: Gaussian Mixture Model and EM Algorithm

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Lec 31: K-means clustering.

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Lec 32: Fuzzy K-means clustering

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Lec 33: Hierarchical Agglomerative Clustering and Mean-shift Clustering

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Lec 34: Artificial Neural Networks for Pattern Classification (PART 1)

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Lec 35: Artificial Neural Networks for Pattern Classification (PART 2)

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Lec 36: Artificial Neural Networks for Pattern Classification (PART 3)

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Lec 37: Introduction to Deep Learning and Convolutional Neural Network (CNN)

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Lec 38: Vanishing and Exploding Gradients in Deep Neural Networks

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Lec 39: CNN Architectures - LeNet-5 and AlexNet

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Lec 40: CNN Architectures – VGG 16, GoogLeNet and ResNet

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Lec 41:Generative Adversarial Networks (GAN) - Fundamentals and Applications

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Lec 42: U-Net: Convolutional Networks for Image Segmentation

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Lec 43: Introduction to Autoencoder and Recurrent Neural Networks (RNN)

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Lec 44: Programming Concepts

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Lec 45: Programming Concepts

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Lec 46: Problem Solving Session

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Lec 47: Problem Solving Session

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Lec 48: Problem Solving Session

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