About the course

• Unit 1

Introduction: Machine learning, terminologies in machine learning, Perspectives and issues in machine learning, application of Machine learning

Types of machine learning: supervised, unsupervised, semi-supervised learning

Review of probability, Basic Linear Algebra in Machine Learning Techniques

Dataset and its types,Data preprocessing

Bias and Variance in Machine learning

Function approximation

Overfittng

• Unit 2

Regression Analysis in Machine Learning: Introduction to regression and its terminologies,Types of regression,Logistic Regression

Simple Linear regression: Introduction to Simple Linear Regression and its assumption, Simple Linear Regression Model Building

Ordinary Least square estimation

Properties of the least-squares estimators and the fitted regression model

Interval estimation in simple linear regression , Residuals

Multiple Linear Regression:Multiple linear regression model and its assumption

Interpret Multiple Linear Regression Output(R-Square, Standard error, F, Significance F, Cofficient P values)

Access the fit of multiple linear regression model (R squared, Standard error)

Feature Selection and Dimensionality Reduction: PCA

LDA, ICA

• Unit 3

Introduction to Classification and Classification Algorithms: What is Classification? General Approach to Classification, k-Nearest Neighbor Algorithm

Random Forests

Fuzzy Set Approaches

Support Vector Machine: Introduction

Types of support vector kernel – (Linear kernel, polynomial kernel, and Gaussiankernel)

Hyperplane – (Decision surface)

Properties of SVM

Issues in SVM

Decision Trees: Decision tree learning algorithm

ID-3algorithm

Inductive bias

Entropy and information theory

Information gain

Issues in Decision tree learning

Bayesian Learning - Bayes theorem

Concept learning

Bayes Optimal Classifier

Naïve Bayes classifier

Bayesian belief networks

EM algorithm

Ensemble Methods: Bagging

Boosting and AdaBoost

XBoost

Classification Model Evaluation and Selection: Sensitivity

Specificity

Positive Predictive Value

Negative Predictive Value

Lift Curves and Gain Curves

ROC Curves

Misclassification Cost Adjustment to Reflect Real-World Concerns

Decision Cost/Benefit Analysis

• Unit 4

Introduction to Cluster Analysis and Clustering Methods: The Clustering Task and the Requirements for Cluster Analysis

Overview of Some Basic Clustering Methods:-k-Means Clustering

k-Medoids Clustering

Density-Based Clustering: DBSCAN - Density-Based Clustering Based on Connected Regions with High Density

Gaussian Mixture Model algorithm

Balance Iterative Reducing and Clustering using Hierarchies (BIRCH)

Affinity Propagation clustering algorithm

Mean-Shift clustering algorithm

ordering Points to Identify the Clustering Structure (OPTICS) Algorithm

Agglomerative Hierarchy clustering algorithm

Divisive Hierarchical

Measuring Clustering Goodness