MACHINE LEARNING (BCS602)

MACHINE LEARNING

Course Code BCS602 
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.

Module-2

Understanding Data – 2: Bivariate Data and Multivariate Data, Multivariate Statistics, Essential Mathematics for Multivariate Data, Feature Engineering and Dimensionality Reduction Techniques.

Basic Learning Theory: Design of Learning System, Introduction to Concept of Learning, Modelling in

Machine Learning.

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.

Decision Tree Learning: Introduction to Decision Tree Learning Model, Decision Tree Induction Algorithms.

Module-4

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

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

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.

Module-5

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

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

Reinforcement Learning: Overview of Reinforcement Learning, Scope of Reinforcement Learning,

Reinforcement Learning as Machine Learning, Components of Reinforcement Learning, Markov Decision

Process, Multi-Arm Bandit Problem and Reinforcement Problem Types, Model-based Learning, Model Free

Methods, Q-Learning, SARSA Learning.

Suggested Learning Resources:

Books

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

Reference Books

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

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

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

Burkov, 2019.