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CY-701 RGPV B.Tech

Machine Learning (ML)

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

Previous Year Papers

3 papers, latest Dec 2025

Free
See all 3 papers

Syllabus, notes and important questions for CY-701

Syllabus units

Topics as the official RGPV syllabus lists them.

Unit 1 Introduction to Machine Learning 16 topics
  • introduction to machine learning
  • scope and limitations of machine learning
  • regression
  • probability
  • statistics
  • 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 Fundamentals 17 topics
  • linearity vs non‑linearity
  • activation functions (sigmoid, ReLU, etc.)
  • weights and bias
  • loss function
  • gradient descent
  • multilayer network
  • back‑propagation
  • weight initialization
  • training and testing of networks
  • unstable gradient problem
  • auto‑encoders
  • batch normalization
  • dropout
  • L1 regularization
  • L2 regularization
  • momentum
  • tuning hyper‑parameters
Unit 2 notes
Unit 3 Convolutional Neural Networks 16 topics
  • convolutional neural network (CNN)
  • flattening
  • subsampling
  • padding
  • stride
  • convolution layer
  • pooling layer
  • loss layer
  • 1x1 convolution (depthwise) layer
  • inception network
  • input channels
  • transfer learning
  • one‑shot learning
  • dimension reduction
  • implementation of CNN with TensorFlow
  • implementation of CNN with Keras
Unit 3 notes
Unit 4 Recurrent Neural Networks and Reinforcement Learning 16 topics
  • recurrent neural network (RNN)
  • long short‑term memory (LSTM)
  • gated recurrent unit (GRU)
  • translation models
  • beam search and beam width
  • BLEU score
  • attention model
  • reinforcement learning (RL)
  • RL framework
  • Markov decision process (MDP)
  • Bellman equations
  • value iteration
  • policy iteration
  • actor‑critic model
  • Q‑learning
  • SARSA
Unit 4 notes
Unit 5 Support Vector Machines and Applications 6 topics
  • support vector machines (SVM)
  • Bayesian learning
  • application of machine learning in computer vision
  • application of machine learning in speech processing
  • application of machine learning in natural language processing
  • case study: ImageNet competition
Unit 5 notes
Read the full syllabus

Exam insights

Counted from 3 past CY-701 papers.

Marks asked per paper, by unit

  • Unit 1 Introduction to machine learning 18 marks
  • Unit 2 Linearity vs non linearity, activation functions and training 19 marks
  • Unit 3 Convolutional neural network 23 marks
  • Unit 4 Recurrent neural network and Reinforcement Learning 23 marks
  • Unit 5 Support Vector Machines, Bayesian learning and applications 19 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 role of backpropagation in training a multilayer network and how weights are adjusted through successive layers.

    Asked 2 times: 2025, 2024

  2. Explain the Inception network architecture and how Inception modules use multiple kernel sizes simultaneously to improve efficiency.

    Asked 2 times: 2025, 2024

  3. Explain the applications of machine learning in speech recognition, synthesis and speech processing.

    Asked 2 times: 2025, 2024

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