Machine Learning — Winter 2024

Answer following: 1. Define human learning. 2. Weather forecasting is _____________ task. (Regression/Classification) 3. Define ROC.

Discuss the use of histogram in effective visualization of attributes with example.

Explain types of data supported by machine learning.

Write short note on bootstrap sampling.

Explain under-fitting and over-fitting in machine learning model.

Define feature construction. Discuss the need of feature construction with an example.

Discuss the measures of feature relevance and redundancy.

Explain data quality and data remediation in detail.

Write down the limitations of holdout method. Explain k-fold cross validation method.

Discuss bagging and boosting in detail.

Explain feature selection process in detail. List out the feature selection approaches.

Differentiate supervised learning and unsupervised learning.

Explain decision tree algorithm with its applications, strengths and weaknesses.

List out the classification learning steps. Explain in detail.

Define regression analysis. Discuss the assumptions made in regression analysis.

Write short note on: 1. Maximum Margin Hyperplane 2.DBSCAN

Explain logistic regression in detail.

Write and discuss apriori algorithm for association rule learning.

Discuss various partition based clustering techniques in detail.