Support Vector Machine Fundamentals

Computer Science

2024-01-17

Lessons List

Machine Learning And Deep Learning Fundamentals And Applications Introduction Video

Lec 1 Introduction to Machine Learning

Lec 2 Performance Measures of Classification

Lec 3 Bias Variance Tradeoff

Lec 4 Regression

Lec 5 Bayesian Decision Theory

Lec 6 Bayesian Decision Theory

Lec 7 Bayes Decision Theory Binary Features

Lec 8 Bayesian Decision Theory

Lec 9 Bayesian Decision Theory

Lec 10 Bayesian Belief Networks

Lec 11 Parameter Estimation and Maximum Likelihood Estimation

Lec 12 Parameter Estimation and Bayesian Estimation

Lec 13 Concept of non parametric techniques

Lec 14 Density Estimation by Parzen Window

Lec 15 Parzen Window and K nearest neighbor algorithm

Lec 16 Linear Discriminant Functions and Perceptron Criteria Part I

Lec 17 Linear Discriminant Functions and Perceptron Criteria Part II

Lec 18 Linear Discriminant Functions and Perceptron Criteria Part III

Lec 19 Support Vector Machine Part I

Lec 20 Support Vector Machine Part II

Lec 21 Logistic Regression

Lec 22 Decision Tree

Lec 23 Hidden Markov Model HMM

Lec 24 Ensemble Classifiers Part I

Lec 25 Ensemble Classifiers Part II

Lec 26 Dimensionality Problem and Principal Component Analysis

Lec 27 Principal Component Analysis

Lec 28 Linear Discriminant Analysis LDA Part I

Lec 29 Linear Discriminant Analysis LDA Part II

Lec 30 Gaussian Mixture Model and EM Algorithm

Lec 31 K means clustering

Lec 32 Fuzzy K means clustering

Lec 33 Hierarchical Agglomerative Clustering and Mean shift Clustering

Lec 34 Artificial Neural Networks for Pattern Classification PART 1

Lec 35 Artificial Neural Networks for Pattern Classification PART 2

Lec 36 Artificial Neural Networks for Pattern Classification PART 3

Lec 37 Introduction to Deep Learning and Convolutional Neural Network CNN

Lec 38 Vanishing and Exploding Gradients in Deep Neural Networks

Lec 39 CNN Architectures LeNet 5 and AlexNet

Lec 40 CNN Architectures – VGG 16 GoogLeNet and ResNet

Lec 41 Generative Adversarial Networks GAN Fundamentals and Applications

Lec 42 U Net Convolutional Networks for Image Segmentation

Lec 43 Introduction to Autoencoder and Recurrent Neural Networks RNN

Lec 44 Programming Concepts

Lec 45 Programming Concepts

Lec 46 Problem Solving Session

Lec 47 Problem Solving Session

Lec 48 Problem Solving Session

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Ahmad Zakaria

Very Usefull

2025-03-25

Babeetha Selvamani

The course was very explanatory and the professors gave a detailed approach.

2024-01-19

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Support Vector Machine course, In this course we will learn about the Support Vector Machine algorithm, its applications in classification, and optimization techniques for effective data separation.

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Support Vector Machine Fundamentals

Computer Science

Machine Learning And Deep Learning Fundamentals And Applications Introduction Video

Lec 1 Introduction to Machine Learning

Lec 2 Performance Measures of Classification

Lec 3 Bias Variance Tradeoff

Lec 4 Regression

Lec 5 Bayesian Decision Theory

Lec 6 Bayesian Decision Theory

Lec 7 Bayes Decision Theory Binary Features

Lec 8 Bayesian Decision Theory

Lec 9 Bayesian Decision Theory

Lec 10 Bayesian Belief Networks

Lec 11 Parameter Estimation and Maximum Likelihood Estimation

Lec 12 Parameter Estimation and Bayesian Estimation

Lec 13 Concept of non parametric techniques

Lec 14 Density Estimation by Parzen Window

Lec 15 Parzen Window and K nearest neighbor algorithm

Lec 16 Linear Discriminant Functions and Perceptron Criteria Part I

Lec 17 Linear Discriminant Functions and Perceptron Criteria Part II

Lec 18 Linear Discriminant Functions and Perceptron Criteria Part III

Lec 19 Support Vector Machine Part I

Lec 20 Support Vector Machine Part II

Lec 21 Logistic Regression

Lec 22 Decision Tree

Lec 23 Hidden Markov Model HMM

Lec 24 Ensemble Classifiers Part I

Lec 25 Ensemble Classifiers Part II

Lec 26 Dimensionality Problem and Principal Component Analysis

Lec 27 Principal Component Analysis

Lec 28 Linear Discriminant Analysis LDA Part I

Lec 29 Linear Discriminant Analysis LDA Part II

Lec 30 Gaussian Mixture Model and EM Algorithm

Lec 31 K means clustering

Lec 32 Fuzzy K means clustering

Lec 33 Hierarchical Agglomerative Clustering and Mean shift Clustering

Lec 34 Artificial Neural Networks for Pattern Classification PART 1

Lec 35 Artificial Neural Networks for Pattern Classification PART 2

Lec 36 Artificial Neural Networks for Pattern Classification PART 3

Lec 37 Introduction to Deep Learning and Convolutional Neural Network CNN

Lec 38 Vanishing and Exploding Gradients in Deep Neural Networks

Lec 39 CNN Architectures LeNet 5 and AlexNet

Lec 40 CNN Architectures – VGG 16 GoogLeNet and ResNet

Lec 41 Generative Adversarial Networks GAN Fundamentals and Applications

Lec 42 U Net Convolutional Networks for Image Segmentation

Lec 43 Introduction to Autoencoder and Recurrent Neural Networks RNN

Lec 44 Programming Concepts

Lec 45 Programming Concepts

Lec 46 Problem Solving Session

Lec 47 Problem Solving Session

Lec 48 Problem Solving Session

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