Machine Learning (CS-601) - Important Questions

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

Explain data preprocessing in detail. Discuss data cleaning, handling missing values, normalization and its importance for machine learning models.

What is gradient descent in machine learning optimization? Explain its working, types and role in minimizing loss function.

Compare L1 and L2 regularization in detail. Explain how regularization controls overfitting in neural networks.

Explain the architecture and operation of a Convolutional Neural Network (CNN), including convolution, pooling, dense and loss layers, downsampling, increasing filters and hierarchical feature extraction.

Define padding in CNNs and explain the commonly used types of padding and how padding works.

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

Explain the structure/architecture of an LSTM unit and how its gates control information flow to process sequential data.

Explain the role and applications of machine learning in computer vision.

Explain the applications and utilization of machine learning algorithms in speech processing.

Explain different types of machine learning models - supervised, unsupervised and reinforcement learning with suitable examples.

Explain multilayer neural networks / multilayer perceptron model in detail, including architecture with diagram, backpropagation, weight initialization, training and testing.

Explain hyperparameter tuning, its techniques, importance for model performance, and challenges.

Explain transfer learning - its benefits, transferable features and applications - and the Inception network architecture in detail.

Explain the concept and working principle of Markov Decision Process (MDP) in detail.

Explain Bayes' theorem and the principles of Bayesian learning including posterior probability with an example.