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Deep Learning (BAI701) AKTU syllabus – unit-wise topics

The complete Deep Learning syllabus for AKTU, unit by unit. Next to each topic you can see how often it has appeared in past papers, so you know where the marks are.

Unit 1: Introduction to Neural Networks

  • Introduction to Machine Learning – asked 2 times
  • Linear Models (SVM, Perceptron, Logistic Regression) – asked 8 times
  • Neural Networks & Shallow Networks – asked 5 times
  • Training a Network (Loss, Backprop, SGD) – asked 10 times
  • Universal Function Approximation – asked 2 times

Unit 2: Deep Networks

  • History & Theory of Deep Learning – asked 8 times
  • Regularization & Batch Normalization – asked 4 times
  • Convolutional Networks – asked 9 times
  • Generative Adversarial Networks (GAN) – asked 4 times
  • Semi-supervised Learning – asked 3 times

Unit 3: Dimensionality Reduction

  • Linear Dimensionality Reduction (PCA, LDA) – asked 7 times
  • Autoencoders – asked 4 times
  • ConvNet Architectures (AlexNet, VGG, ResNet) – asked 6 times
  • Training a ConvNet (Weight Init, Hyperparameters) – asked 5 times

Unit 4: Optimization & Generalization

  • Optimization in Deep Learning – asked 10 times
  • Generalization in Neural Networks – asked 3 times
  • Spatial Transformer Networks – asked 1 times
  • Recurrent Networks & LSTM – asked 6 times
  • Deep Reinforcement Learning – asked 3 times
  • Computational & Artificial Neuroscience – asked 2 times

Unit 5: Case Studies & Applications

  • Image Classification & Detection – asked 4 times
  • NLP & Word2Vec – asked 3 times
  • Audio WaveNet – asked 5 times
  • Face Recognition – asked 3 times
  • Image Captioning – asked 3 times
  • Bioinformatics – asked 2 times