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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishVirtual ClassroomVideo and Text Based

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 1000yesIIT Guwahati (IITG)

The Syllabus

Introduction
  • Introduction to ML
  • Performance Measures
  • Bias-Variance Trade off
  • Linear Regression

Bayes Decision Theory
  • Bayes Decision Theory
  • Normal Density and Discriminant Function
  • Bayes Decision Theory - Binary Features
  • Bayesian Belief Network

Parametric and Non- Parametric Density Estimation
  • Parametric and Non- Parametric Density Estimation – ML and Bayesian Estimation
  • Parzen Window and KNN

Perceptron Criteria and Discriminative Models
  • Perceptron Criteria
  • Discriminative models
  • Support Vector Machines (SVM)

Logistic Regression, Decision Trees and Hidden Markov Model
  • Logistic Regression
  • Decision trees
  • Hidden Markov Model (HMM)

Ensemble methods
  • Ensemble methods: Ensemble strategies
  • Boosting and Bagging
  • Random Forest

Dimensionality Problem
  • Dimensionality Problem
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)

Mixture Model and Clustering
  • Concept of mixture model
  • Gaussian mixture model
  • Expectation Maximization Algorithm
  • K- means clustering

Clustering
  • Fuzzy K-means clustering
  • Hierarchical Agglomerative Clustering
  • Mean-shift clustering

Neural Network
  • Neural network: Perceptron
  • Multilayer network
  • Backpropagation
  • RBF Neural Network
  • Applications

Introduction to Deep Neural Networks
  • Introduction to Deep Learning, Convolutional Neural Networks (CNN)
  • Vanishing and Exploding Gradients in Deep Neural Networks
  • LeNet - 5
  • AlexNet
  • VGGNet
  • GoogleNet
  • ResNet

Recent Trends in Deep Learning
  • Generative Adversarial Networks (GAN)
  • Auto Encoders and Relation to PCA
  • Recurrent Neural Networks
  • U-Net
  • Applications and Case studies

Instructors

Articles

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