Machine Learning (CY-701) - Important Questions

Explain the major limitations of machine learning.

Explain the role and importance of data preprocessing in machine learning and why it is needed.

What is the need for data augmentation? Discuss about data augmentation.

Explain the role of backpropagation in training a multilayer network and how weights are adjusted through successive layers.

What is an activation function? Explain ReLU function and compute the output of ReLU function for x = -3, 0, 5.

Explain the working of batch normalization. Write its benefits and usage areas.

Explain the concept of padding in CNNs, including types such as valid and same padding and their influence on preservation of spatial features.

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

Explain the implementation of Convolutional Neural Network (CNN) in TensorFlow.

Explain the concept of Recurrent Neural Network (RNN).

Explain the difference between the SARSA and Q-learning algorithms in terms of updating Q-values.

Explain the role of beam width in beam search and how it affects performance.

Explain what a Support Vector Machine is and how it finds the optimal maximum-margin hyperplane for classification with a linear SVM.

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

Write short notes on any two of the following: (i) 1x1 convolution (ii) Actor-critic model (iii) Bayesian learning (iv) Natural language processing.