R23
CSE · 3-2
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-09
Machine Learning
JNTUK R23 · CSE · semester 3-2 · syllabus
This JNTUK R23 professional elective is listed for Computer Science and Engineering (CSE). It is listed in semester 3-2. It carries 3 credits. The published coverage runs across 5 units, from Introduction to Machine Learning through Clustering.
Verified vs. published syllabus
Checked 9 Jul 2026
SOURCE DOCKET
Published evidence
✓ Verified
Checked 9 Jul 2026
Record 5 published units
Verification scope Subject name, credits, unit-wise syllabus, and course outcomes are sourced directly from JNTUK's own official R23 course structure and syllabus document, not an autonomous college's adaptation.
5 units, from Introduction to Machine Learning to Clustering. Tick off units as you cover them — your progress stays on this device.
Unit-wise syllabus
0 / 5 COVERED
UNIT 01 — 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 02 — Nearest Neighbor-Based Models
Introduction to proximity measures; distance measures; non-metric similarity functions · Proximity between binary patterns; classification algorithms based on distance measures · K-nearest neighbor classifier; radius distance nearest neighbor algorithm; KNN regression · Performance of classifiers and of regression algorithms
UNIT 03 — Models Based on Decision Trees and the Bayes Classifier
Decision trees for classification; impurity measures and properties · Regression based on decision trees; bias-variance trade-off; random forests for classification and regression · 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 04 — Linear Discriminants for Machine Learning
Introduction to linear discriminants; linear discriminants for classification · Perceptron classifier and perceptron learning algorithm · Support vector machines; linearly non-separable case; non-linear SVM; kernel trick · Logistic regression and linear regression · Multi-layer perceptrons (MLPs); backpropagation for training an MLP
UNIT 05 — Clustering
Introduction to clustering; partitioning of data; matrix factorization · Clustering of patterns: divisive, agglomerative and partitional clustering · K-means clustering; soft partitioning and soft clustering; fuzzy C-means clustering · Rough clustering and rough K-means clustering algorithm · Expectation maximization-based clustering; spectral clustering