IT-802 (A) RGPV B.Tech
Machine Learning (ML)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
2 papers, latest Jun 2025
Syllabus, notes and important questions for IT-802 (A)
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction 8 topics
- Introduction
- Examples of various Learning Paradigms
- Perspectives and Issues
- Concept Learning
- Version Spaces
- Finite and Infinite Hypothesis Spaces
- PAC Learning
- VC Dimension
Unit 2 Supervised Learning Algorithms 7 topics
- Learning a Class from Examples
- Linear, Non‑linear, Multi‑class and Multi‑label classification
- Decision Trees: ID3, Classification and Regression Trees (CART)
- Regression: Linear Regression, Multiple Linear Regression, Logistic Regression
- Neural Networks: Introduction, Perceptron, Multilayer Perceptron
- Support vector machines: Linear and NonLinear, Kernel Functions
- K‑Nearest Neighbors
Unit 3 Ensemble Learning 6 topics
- Ensemble Learning Model Combination Schemes
- Voting
- Error‑Correcting Output Codes
- Bagging: Random Forest Trees
- Boosting: Adaboost
- Stacking
Unit 4 Unsupervised Learning 9 topics
- Introduction to clustering
- Hierarchical: AGNES, DIANA
- Partitional: K‑means clustering, KMode Clustering
- Self‑Organizing Map
- Expectation Maximization
- Gaussian Mixture Models
- Principal Component Analysis (PCA)
- Locally Linear Embedding (LLE)
- Factor Analysis
Unit 5 Probabilistic Learning 5 topics
- Bayesian Learning
- Bayes Optimal Classifier
- Naïve Bayes Classifier
- Bayesian Belief Networks
- Mining Frequent Patterns