MACHINE LEARNING (BAI602)

MACHINE LEARNING

Course Code BAI602 
CIE Marks 50
Teaching Hours/Week (L: T:P: S) 4:0:0:0 
SEE Marks 50
Total Hours of Pedagogy 50 
Total Marks 100
Credits 04 
Exam Hours 03
Examination type (SEE) Theory 

Module-1

Introduction: Need for Machine Learning, Machine Learning Explained, Machine Learning in Relation

to other Fields, Types of Machine Learning, Challenges of Machine Learning, Machine Learning Process, Machine Learning Applications.

Understanding Data – 1: Introduction, Big Data Analysis Framework, Descriptive Statistics, Univariate

Data Analysis and Visualization.

Chapter-1, 2 (2.1-2.5)

Module-2

Understanding Data – 2: Bivariate Data and Multivariate Data, Multivariate Statistics, Essential

Mathematics for Multivariate Data, Feature Engineering and Dimensionality Reduction Techniques.

Testing Machine Learning Algorithms: Overfitting , Training, Testing, and Validation Sets ,The

Confusion Matrix , Accuracy Metrics , The Receiver Operator Characteristic (ROC) Curve ,Unbalanced Datasets , Measurement Precision

Textbook-1: Chapter -2 (2.6-2.8, 2.10), Text book-2 (2.2)

Module-3

Similarity-based Learning: Nearest-Neighbor Learning, Weighted K-Nearest-Neighbor Algorithm,

Nearest Centroid Classifier, Locally Weighted Regression (LWR).

Regression Analysis: Introduction to Regression, Introduction to Linear Regression, Multiple Linear

Regression, Polynomial Regression, Logistic Regression.

Chapter-4 (4.2-4.5), Chapter-5 (5.1-5.3, 5.5-5.7)

Module-4

Decision Tree Learning: Introduction to Decision Tree Learning Model, Decision Tree Induction Algorithms. Validating and pruning of Decision trees.

Bayesian Learning: Introduction to Probability-based Learning, Fundamentals of Bayes Theorem,

Classification Using Bayes Model, Naïve Bayes Algorithm for Continuous Attributes.

Chapter-6 (6.1, 6.3), Chapter-8 (8.1-8.4)

Module-5

Artificial Neural Networks: Introduction, Biological Neurons, Artificial Neurons, Perceptron and Learning Theory, Types of Artificial Neural Networks, Popular Applications of Artificial Neural Networks, Advantages and Disadvantages of ANN, Challenges of ANN.

Clustering Algorithms: Introduction to Clustering Approaches, Proximity Measures, Hierarchical

Clustering Algorithms, Partitional Clustering Algorithm, Density-based Methods, Grid-based Approach.

Chapter-10 (10.1-10.5, 10.9-10.11), Chapter -13 (13.1-13.6)

Suggested Learning Resources:

Books

1. S Sridhar, M Vijayalakshmi, “Machine Learning”, OXFORD University Press 2021, First Edition.

2. Stephen Marsland, “Machine Learning - An Algorithmic Perspective”, Second Edition, CRC Press - Taylor and Francis Group, 2015 .

Reference Books

1. T. M. Mitchell, “Machine Learning”, McGraw Hill, 1997.

2. Murty, M. N., and V. S. Ananthanarayana. Machine Learning: Theory and Practice, Universities Press, 2024.

3. Burkov, Andriy. The hundred-page machine learning book. Vol. 1. Quebec City, QC, Canada: Andriy Burkov, 2019.