R23
IT · 3-2
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-11
Machine Learning
JNTUK R23 · IT · semester 3-2 · syllabus
This JNTUK R23 professional core course is listed for Information Technology (IT). 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 11 Jul 2026
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Published evidence
✓ Verified
Checked 11 Jul 2026
Record 5 published units
Verification scope Subject name, credits, and unit-wise syllabus are sourced directly from JNTUK's official R23 course structure and syllabus document.
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 and paradigms for ML: learning by rote, learning by induction and reinforcement learning · Types of data and matching · Stages in machine learning: data acquisition, feature engineering, data representation and model selection · Model learning, model evaluation, model prediction, search and learning, and data sets
UNIT 02 — Nearest Neighbor-Based Models
Proximity measures: distance measures, non-metric similarity functions and proximity between binary patterns · Classification algorithms based on distance measures · K-Nearest Neighbor classifier, radius distance nearest neighbor algorithm and KNN regression · Performance of classifiers and performance 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, the bias-variance trade-off, and random forests for classification and regression · The Bayes classifier: Bayes' rule and inference, and the Bayes classifier and its optimality · Multi-class classification, class conditional independence and the Naive Bayes classifier (NBC)
UNIT 04 — Linear Discriminants for Machine Learning
Linear discriminants for classification: the perceptron classifier and the perceptron learning algorithm · Support vector machines: the linearly non-separable case, non-linear SVM and the kernel trick · Logistic regression and linear regression · Multi-layer perceptrons (MLPs) and backpropagation for training an MLP
UNIT 05 — Clustering
Partitioning of data, matrix factorization and clustering of patterns · Divisive clustering, agglomerative clustering and partitional clustering · K-Means clustering, soft partitioning and soft clustering, and Fuzzy C-Means clustering · Rough clustering and the rough K-Means clustering algorithm, Expectation-Maximization-based clustering, and spectral clustering