Machine Learning (AL-405) - Important Questions

Explain the concepts of hypothesis space and inductive bias in machine learning and their importance in model learning.

Explain machine learning, its scope, limitations and real-world applications with suitable examples.

Explain what Principal Component Analysis (PCA) is, with examples, and how it helps in dimensionality reduction of data.

Explain the multi-layer perceptron model with a neat diagram.

Define backpropagation and explain its algorithm for computing gradients and updating weights in a neural network, with example and diagram.

Explain parallel processing and parallel distributed processing in neural networks and how it enables efficient computation, with diagram.

Explain the Random Forest algorithm and how it combines multiple decision trees for classification and regression.

Define decision tree and explain the working of decision tree / ID3 / CART learning algorithm with an example.

Compare linear regression and logistic regression with suitable examples.

Explain the goal of SVM for classification of linearly separable data, including hyperplane, margin maximization and how to compute the margin.

Explain the k-means clustering algorithm in detail, including steps, objective function, how it partitions data, limitations and effect of choice of k.

Explain the Expectation-Maximization (EM) algorithm, including the E-step and M-step, why it is needed for missing/incomplete data, with an example such as training Gaussian Mixture Models.

Explain how adaptive hierarchical clustering works and how it adaptively determines the number of clusters from the data.

Explain resampling methods and cross-validation methods used in machine learning.

Explain hypothesis testing in machine learning with examples.

Explain measuring classifier performance using Accuracy, Precision, Recall and F1-Score.