Machine learning course

UNIT‐I

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, Overfitting

UNIT‐II

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, Coefficient P values)

Access the fit of multiple

Linear regression model (R squared, Standard error)

Feature Selection and Dimensionality Reduction: PCA, LDA, ICA

UNIT‐III

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 Gaussian kernel), Hyperplane – (Decision surface), Properties of SVM, and 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 and 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 – IV

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