Machine Learning and Deep Learning - Fundamentals and Applications (English)

   Friday, April 12, 2024   

Starting a new Lecture Notes Series on Machine Learning and Deep Learning - Fundamentals and Applications

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Channel Name: NPTEL IIT Guwahati

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Machine Learning and Deep Learning - Fundamentals and Applications By Lecture Notes together!

Lecture 1: Machine Learning And Deep Learning - Fundamentals And Applications

Lecture 2: Lec 1: Introduction to Machine Learning

Lecture 3: Lec 2: Performance Measures of Classification

Lecture 4: Lec 3: Bias-Variance Tradeoff

Lecture 5: Lec 4: Regression

Lecture 6: Lec 5: Bayesian Decision Theory

Lecture 7: Lec 6: Bayesian Decision Theory

Lecture 8: Lec 7: Bayes Decision Theory - Binary Features

Lecture 9: Lec 8: Bayesian Decision Theory

Lecture 10: Lec 9: Bayesian Decision Theory

Lecture 11: Lec 10: Bayesian Belief Networks

Lecture 12: Lec 11: Parameter Estimation and Maximum Likelihood Estimation

Lecture 13: Lec 12: Parameter Estimation and Bayesian Estimation

Lecture 14: Lec 13: Concept of non-parametric techniques

Lecture 15: Lec 14: Density Estimation by Parzen Window

Lecture 16: Lec 15: Parzen Window and K nearest neighbor algorithm

Lecture 17: Lec 16: Linear Discriminant Functions and Perceptron Criteria (Part I)

Lecture 18: Lec 17: Linear Discriminant Functions and Perceptron Criteria (Part II)

Lecture 19: Lec 18: Linear Discriminant Functions and Perceptron Criteria (Part III)

Lecture 20: Lec 19: Support Vector Machine (Part I)

Lecture 21: Lec 20: Support Vector Machine (Part II)

Lecture 22: Lec 21: Logistic Regression

Lecture 23: Lec 22: Decision Tree

Lecture 24: Lec 23: Hidden Markov Model (HMM)

Lecture 25: Lec 24: Ensemble Classifiers (Part I)

Lecture 26: Lec 25: Ensemble Classifiers (Part II)

Lecture 27: Lec 26: Dimensionality Problem and Principal Component Analysis

Lecture 28: Lec 27: Principal Component Analysis

Lecture 29: Lec 28: Linear Discriminant Analysis (LDA) (Part I)

Lecture 30: Lec 29: Linear Discriminant Analysis (LDA) (Part II)

Lecture 31: Lec 30: Gaussian Mixture Model and EM Algorithm

Lecture 32: Lec 31: K-means clustering.

Lecture 33: Lec 32: Fuzzy K-means clustering

Lecture 34: Lec 33: Hierarchical Agglomerative Clustering and Mean-shift Clustering

Lecture 35: Lec 34: Artificial Neural Networks for Pattern Classification (PART 1)

Lecture 36: Lec 35: Artificial Neural Networks for Pattern Classification (PART 2)

Lecture 37: Lec 36: Artificial Neural Networks for Pattern Classification (PART 3)

Lecture 38: Lec 37: Introduction to Deep Learning and Convolutional Neural Network (CNN)

Lecture 39: Lec 38: Vanishing and Exploding Gradients in Deep Neural Networks

Lecture 40: Lec 39: CNN Architectures - LeNet-5 and AlexNet

Lecture 41: Lec 40: CNN Architectures – VGG 16, GoogLeNet and ResNet

Lecture 42: Lec 41:Generative Adversarial Networks (GAN) - Fundamentals and Applications

Lecture 43: Lec 42: U-Net: Convolutional Networks for Image Segmentation

Lecture 44: Lec 43: Introduction to Autoencoder and Recurrent Neural Networks (RNN)

Lecture 45: Lec 44: Programming Concepts

Lecture 46: Lec 45: Programming Concepts

Lecture 47: Lec 46: Problem Solving Session

Lecture 48: Lec 47: Problem Solving Session

Lecture 49: Lec 48: Problem Solving Session