Machine Learning and Deep Learning Fundamentals and Applications
Understand how algorithms enables computers to learn patterns and make decisions for various applications.
Online
12 WeeksQuick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Virtual Classroom | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT Guwahati (IITG) |
The fees for the course Machine Learning and Deep Learning Fundamentals and Applications is :
| Fees components | Amount |
| Exam fees | Rs. 1,000 |
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