AD-502 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 Nov 2023
Syllabus, notes and important questions for AD-502
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction to Machine Learning 12 topics
- Introduction to machine learning
- Machine learning life cycle
- Types of machine learning system (supervised, unsupervised, batch, online, instance‑based, model‑based)
- Scope and limitations
- Challenges of machine learning
- Data visualization
- Hypothesis function and testing
- Data pre‑processing
- Data augmentation
- Normalizing data sets
- Bias‑Variance tradeoff
- Relationship between AI, ML, DL and DS
Unit 2 Clustering in Machine Learning 12 topics
- Clustering in machine learning
- Partitioning clustering
- Distribution model‑based clustering
- Hierarchical clustering
- Fuzzy clustering
- Birch algorithm
- CURE algorithm
- Gaussian mixture models
- Expectation maximization
- Parameter estimation – maximum likelihood estimation (MLE)
- Parameter estimation – maximum a posteriori (MAP)
- Applications of clustering
Unit 3 Classification Algorithms 10 topics
- Logistic regression
- Decision tree classification
- Neural networks
- K‑Nearest Neighbors (K‑NN)
- Support vector machine
- Naïve Bayes (Gaussian, Multinomial, Bernoulli)
- Confusion matrix
- Classification accuracy
- Precision, recall, F1‑score
- Support
Unit 4 Ensemble Learning and Random Forest 13 topics
- Introduction to ensemble learning
- Max voting
- Averaging
- Weighted average
- Voting classifiers
- Bagging
- Pasting
- Out‑of‑bag evaluation
- Random patches
- Random subspaces
- Random forests (extra‑trees, feature importance)
- Boosting (AdaBoost, gradient boosting)
- Stacking
Unit 5 Dimensionality Reduction 14 topics
- Curse of dimensionality
- Projection‑based dimensionality reduction
- Manifold learning
- Principal component analysis (PCA) – preserving variance
- Principal components
- Projecting to d dimensions
- Explained variance ratio
- Choosing number of dimensions
- PCA for compression
- Randomized PCA
- Incremental PCA
- Kernel PCA – kernel selection and hyper‑parameter tuning
- Learning theory – PAC model
- Learning theory – VC model