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CS-601 RGPV B.Tech

Machine Learning (ML)

Previous year papers and the official syllabus are free to read, no account needed.

Previous Year Papers

6 papers, latest Jun 2026

Free
See all 6 papers

Syllabus, notes and important questions for CS-601

Syllabus units

Topics as the official RGPV syllabus lists them.

Unit 1 Introduction to Machine Learning 15 topics
  • Introduction to machine learning
  • Scope and limitations
  • Regression
  • Probability
  • Statistics and linear algebra for machine learning
  • Convex optimization
  • Data visualization
  • Hypothesis function and testing
  • Data distributions
  • Data preprocessing
  • Data augmentation
  • Normalizing data sets
  • Machine learning models
  • Supervised learning
  • Unsupervised learning
Unit 1 notes
Unit 2 Deep Learning Foundations 17 topics
  • Linearity vs non linearity
  • Activation functions (sigmoid, ReLU, etc.)
  • Weights and bias
  • Loss function
  • Gradient descent
  • Multilayer network
  • Backpropagation
  • Weight initialization
  • Training
  • Testing
  • Unstable gradient problem
  • Autoencoders
  • Batch normalization
  • Dropout
  • L1 and L2 regularization
  • Momentum
  • Tuning hyperparameters
Unit 2 notes
Unit 3 Convolutional Neural Networks 15 topics
  • Convolutional neural network
  • Flattening
  • Subsampling
  • Padding
  • Stride
  • Convolution layer
  • Pooling layer
  • Loss layer
  • 1x1 convolution
  • Inception network
  • Input channels
  • Transfer learning
  • One‑shot learning
  • Dimension reductions
  • Implementation of CNN using TensorFlow, Keras etc.
Unit 3 notes
Unit 4 Recurrent Neural Networks and Reinforcement Learning 16 topics
  • Recurrent neural network
  • Long short‑term memory (LSTM)
  • Gated recurrent unit (GRU)
  • Translation
  • Beam search and width
  • BLEU score
  • Attention model
  • Reinforcement Learning
  • RL framework
  • Markov Decision Process (MDP)
  • Bellman equations
  • Value iteration
  • Policy iteration
  • Actor‑critic model
  • Q‑learning
  • SARSA
Unit 4 notes
Unit 5 Advanced Topics and Applications 6 topics
  • Support Vector Machines
  • Bayesian learning
  • Applications in computer vision
  • Speech processing
  • Natural language processing
  • Case Study: ImageNet Competition
Unit 5 notes
Read the full syllabus

Exam insights

Counted from 6 past CS-601 papers.

Marks asked per paper, by unit

  • Unit 1 Introduction to machine learning 27 marks
  • Unit 2 Linearity vs non linearity 25 marks
  • Unit 3 Convolutional neural network 20 marks
  • Unit 4 Recurrent neural network 19 marks
  • Unit 5 Support Vector Machines 17 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

  1. Explain the architecture and operation of a CNN, including convolution, pooling, dense and loss layers, downsampling, increasing filters, and hierarchical feature extraction.

    Asked 3 times: 2026, 2024, 2023

  2. Explain the definition, scope, limitations and applications of Machine Learning.

    Asked 2 times: 2026, 2025

  3. Compare Value Iteration and Policy Iteration, explaining differences with suitable examples.

    Asked 2 times: 2026, 2024

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