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OIT1706 MACHINE LEARNING AND R PROGRAMMING L T P C
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OBJECTIVES:

 To analyze data by applying machine learning techniques
 Understand basic constructs in R
 Learning and applying basic classification techniques.
 Learning various black box techniques of classification, market basket analysis and clustering
 Evaluating performance of the models

UNIT I INTRODUCTION 9

Introduction to Machine Learning – Need of machine learning-Kinds of machine learning – Steps of
machine learning – choosing a machine learning algorithm – Using R for Machine Learning-Probability
Distributions-Basis Statistics

UNIT II R DATA STRUCTURES 9

Managing and understanding data – Console input and output – Data Types – operators – Functions - R
Data Structures – Vectors – Factors –Lists – Data Frames – Matrices and arrays – import and export
files – Exploring and understanding data – Visualization – Categorical variables exploration – Relations
between variables

UNIT III LEARNING BY CLASSIFICATION 9

Classification – Lazy Learner - K-Nearest Neighbor – Probabilistic Learner - Naïve Bayes – Divide and
Conquer - Decision Trees and Rule based Learning – Understanding classification rules -Forecasting
numerical data – Regression Models – Visualization using Plots - Case Study : Breast Cancer with
KNN , Filtering Mobile phone spam using Naïve Bayes , Risky Bank Loans using Decision Trees ,
Identifying Poisonous Mushrooms with Rules based learners , Predict medical Expense with Linear
Regression

UNIT IV BLACK BOX CLASSIFICATION, MARKET BASKET ANALYSIS AND
CLUSTERING 9

Black Box Learning Methods – SVM – Finding Maximum Margin – Using Kernels for non – linear
spaces - Neural Networks - Market Basket Analysis – Understanding association rules – Apriori
Algorithm – Case Study: Modeling strength of concrete , OCR with SVM, Identification of frequently
purchased groceries with apriori

Clustering – K-Means Algorithm-Partitioning Around Medoids (PAM) –Hierarchical Clustering- Case
Study – Finding Teen Segments of Market

UNIT V PERFORMANCE EVALUATION 9

Evaluating Model Performance – Measuring performance for classifier – Beyond Accuracy – Kappa –
Sensitivity and Specificity – Precision and recall – F-Measure – Visualization with ROC Curve –
Estimate future performance – Improving Model Performance – Tuning stock models for better
performance – Improving model performance with meta leaners

Curriculum and Syllabus | Open Electives | R 2017 | REC Page 77