Machine Learning — Winter 2023

Answer the following. 1. Define bootstrap sampling. 2. What is deep learning? 3. List the feature selection approaches.

Discuss the concept of over fitting and under fitting in machine learning.

Differentiate supervised learning, unsupervised learning and reinforcement learning.

Discuss human learning vs machine learning.

Discuss hierarchical clustering in brief.

Explain K-fold cross validation method in detail.

List the feature extraction algorithms. Discuss PCA in brief.

Discuss the concept of Bayes’ theorem in brief.

Explain k-Nearest Neighbor algorithm in brief.

Briefly explain measures of feature relevance and feature redundancy.

What are outliers? Discuss the ways to handle the outliers and missing values.

Explain Logistic Regression in brief.

Write short note on K-Means clustering algorithm.

Briefly explain classification learning steps.

Define regression analysis. Write down the assumptions in regression analysis.

List the types of Activation functions. Explain any three.

Explain Apriori algorithm for association rule with appropriate example.

What is a biological neuron? Write a short note on multi layer feed forward network of ANN.

Discuss the steps of backpropagation algorithm.