AL-405 RGPV B.Tech
Machine Learning (ML)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
7 papers, latest Jun 2026
- AL-405 paper, Jun 2026
- AL-405 paper, Jun 2025
- AL-405 paper, Dec 2024
- AL-405 paper, Jun 2024
- AL-405 paper, Nov 2023
- AL-405 paper, Jun 2023
Syllabus, notes and important questions for AL-405
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction to Machine Learning 7 topics
- Introduction to machine learning, scope and limitations
- Machine learning models
- Supervised learning
- Unsupervised learning
- Hypothesis space and inductive bias
- Evaluation methods and cross‑validation
- Dimensionality reduction techniques: subset selection, shrinkage methods, principal component analysis, partial least squares
Unit 2 Neural Networks 10 topics
- Biological inspiration and simulation of neural networks
- Neural network representation
- Neural networks as a paradigm for parallel processing
- Perceptron learning rule
- Training a perceptron
- Multilayer perceptron architecture
- Back‑propagation algorithm
- Training and validation strategies
- Activation functions
- Vanishing and exploding gradients
Unit 3 Supervised Learning Techniques 8 topics
- Decision tree algorithms
- Naïve Bayes classifier
- General classification concepts
- Support vector machines for classification
- Random forest for classification and regression
- Linear regression
- Ordinary least squares regression
- Logistic regression
Unit 4 Unsupervised Learning and Clustering 8 topics
- Clustering fundamentals
- k‑means algorithm
- Adaptive hierarchical clustering
- Gaussian mixture models
- Optimization using evolutionary techniques
- Determining the number of clusters
- Advanced clustering discussions
- Expectation‑maximization algorithm
Unit 5 Design and Analysis of Machine Learning Experiments 8 topics
- Design of machine learning experiments
- Factors, response, and strategy of experimentation
- Guidelines for machine learning experiments
- Cross‑validation and resampling methods
- Measuring classifier performance
- Hypothesis testing for model comparison
- Comparing multiple algorithms
- Comparison over multiple datasets
Exam insights
Counted from 7 past AL-405 papers.
Marks asked per paper, by unit
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Unit 1 Introduction to machine learning 19 marks
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Unit 2 Neural Networks 20 marks
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Unit 3 Supervised Learning Techniques 27 marks
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Unit 4 Unsupervised Learning, Clustering 21 marks
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Unit 5 Design and Analysis of Machine Learning Experiments 20 marks
Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.
Asked again and again
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Explain resampling methods and cross-validation methods used in machine learning.
Asked 4 times: 2026, 2023, 2022
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Explain the Expectation-Maximization (EM) algorithm, including the E-step and M-step, why it is needed for missing/incomplete data, with an example such as training Gaussian Mixture Models.
Asked 4 times: 2024, 2023
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Explain the Random Forest algorithm and how it combines multiple decision trees for classification and regression.
Asked 3 times: 2026, 2024, 2023