VISHNU INSTITUTE OF TECHNOLOGY (AUTONOMOUS) : : BHIMAVARAM
Approved by AICTE, Accredited by NAAC-A++, NBA & Affiliated to JNTUK, Kakinada
B.Tech CSE(AI & ML) (R23 COURSE STRUCTURE & SYLLABUS)
II Year II Semester L T P C
3 0 0 3
MACHINE LEARNING
Course Objectives:
The objectives of the course is to
Define machine learning and its different types (supervised and unsupervised) and
understand their applications.
Apply supervised learning algorithms including decision trees and k-nearest
neighbours (k-NN).
Implement unsupervised learning techniques, such as K-means clustering.
UNIT-I: Introduction to Machine Learning: Evolution of Machine Learning, Paradigms
for ML, Learning by Rote, Learning by Induction, Reinforcement Learning, Types of Data,
Matching, Stages in Machine Learning, Data Acquisition, Feature Engineering, Data
Representation, Model Selection, Model Learning, Model Evaluation, Model Prediction,
Search and Learning, Data Sets.
UNIT-II: Nearest Neighbor-Based Models: Introduction to Proximity Measures, Distance
Measures, Non-Metric Similarity Functions, Proximity Between Binary Patterns, Different
Classification Algorithms Based on the Distance Measures ,K-Nearest Neighbor Classifier,
Radius Distance Nearest Neighbor Algorithm, KNN Regression, Performance of Classifiers,
Performance of Regression Algorithms.
UNIT-III: Models Based on Decision Trees: Decision Trees for Classification, Impurity
Measures, Properties, Regression Based on Decision Trees, Bias–Variance Trade-off,
Random Forests for Classification and Regression.
The Bayes Classifier: Introduction to the Bayes Classifier, Bayes’ Rule and Inference, The
Bayes Classifier and its Optimality, Multi-Class Classification | Class Conditional
Independence and Naive Bayes Classifier (NBC)
UNIT-IV: Linear Discriminants for Machine Learning: Introduction to Linear
Discriminants, Linear Discriminants for Classification, Perceptron Classifier, Perceptron
Learning Algorithm, Support Vector Machines, Linearly Non-Separable Case, Non-linear
SVM, Kernel Trick, Logistic Regression, Linear Regression, Multi-Layer Perceptrons
(MLPs), Backpropagation for Training an MLP.
UNIT-V: Clustering : Introduction to Clustering, Partitioning of Data, Matrix Factorization |
Clustering of Patterns, Divisive Clustering, Agglomerative Clustering, Partitional Clustering,
K-Means Clustering, Soft Partitioning, Soft Clustering, Fuzzy C-Means Clustering, Rough
Clustering, Rough K-Means Clustering Algorithm, Expectation Maximization-Based
Clustering, Spectral Clustering.
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VISHNU INSTITUTE OF TECHNOLOGY (AUTONOMOUS) : : BHIMAVARAM
Approved by AICTE, Accredited by NAAC-A++, NBA & Affiliated to JNTUK, Kakinada
Text Books:
1. “Machine Learning Theory and Practice”, M N Murthy, V S Ananthanarayana,
Universities Press (India), 2024
Reference Books:
1. “Machine Learning”, Tom M. Mitchell, McGraw-Hill Publication, 2017
2. “Machine Learning in Action”,Peter Harrington, DreamTech
3. “Introduction to Data Mining”, Pang-Ning Tan, Michel Stenbach, Vipin Kumar, 7th
Edition, 2019.
