Machine Learning — Summer 2024

Explain, in details, the process of K-fold cross-validation.

Differentiate supervise learning, unsupervised learning & reinforcement learning.

Explain the algorithm for KNN with suitable example.

Explain the concept of Naive Bayesian theorem.

Discuss the random forest model in detail. What are the features of random forest?

Discuss the concept of Bayes’ theorem in brief.

What is feature engineering? Explain, in details, the different aspects of feature engineering?

What is feature selection? Why is it needed? What are the different approaches of feature selection?

List the feature extraction algorithms. Discuss PCA in brief.

What are the broad three categories of clustering techniques? Explain the characteristics of each briefly.

Describe the main difference in the approach of k-means and kmedoids Algorithm with a neat diagram.

Explain the Apriori algorithm for association rule learning with an example.

You are given a set of one-dimensional data points: {5, 10, 15, 20, 25, 30, 35}. Assume that k = 2 and first set of random centroid is selected as {15, 32} and then it is refined with {12, 30}. 1. Create two clusters with each set of centroid mentioned above following the k-means approach. 2. Calculate the SSE for each set of centroid.

Explain in detail Back propagation Neural network.

Write a detail note on learning process in Artificial Neural Network.

Differentiate biological neuron and artificial neuron.

Explain, in details, the process of evaluating the performance of a classification model. Explain the different parameters of measurement.