ML Important Questions on Unit 1 to 6 PDF

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B. Computer Subject: Machine LearningImportant Questions on Unit 1 and 2Sr. No Question Define machine learning and explain the natural language processing? What do you mean by supervised and unsupervised machine learning algorithms? 3.Explain how machine learning works for the following unsupervise...

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B.E. Computer

Subject: Machine Learning

Important Questions on Unit 1 and 2

Sr. No

Question

1.

Define machine learning and explain the natural language processing?

2.

What do you mean by supervised and unsupervised machine learning algorithms?

3.

Explain how machine learning works for the following unsupervised machine learning applications: a. Item Categorization b. Customer Segmentation c. Automatic labeling d. Similarity detection

4.

Explain the concept of adaptive learning?

5.

Explain data formats for supervised learning problem with example?

6.

What is Principal Component Analysis (PCA)? When it is used?

7.

What do you mean by dictionary learning? What are its applications?

8.

Illustrate criteria for the creation of dataset in machine learning methods? Explain how to asses missing features in the data set?

9.

Justify the statement “ Raw data has a significant impact on feature engineering process”

10.

Write short note on sparse PCA.

11.

What are the categorical data? What is its significance in classification problems?

12.

With reference to feature engineering, explain data scaling and normalization tasks?

B.E. Computer

Subject: Machine Learning

Important Questions on Unit 3 and 4

Sr. No 1.

Questions What do you mean by linear regression? Which applications are best modeled by linear regression?

2.

Write short note on 1. ROC Curve 2. Bernoulli Naïve Bayes 3. Kernal PCA.

3.

Explain Higher dimensional linear regression with suitable example?

4.

Elaborate Naïve Bayes Classifier working with example?

5.

Explain the nonlinear SVM with example.

6.

Explain Kernel based Classification in detail.

7.

Explain in detail the Ridge regression and the Lasso regression.

8.

What do you mean by linearly separable data and non-linearly separable data?

9.

Discuss in brief the dictionary learning.

10.

Explain isotonic regression and write the applications in brief.

B.E. Computer

Subject: Machine Learning

Important Questions on Unit 5 and 6

Sr. No

Questions

1.

Explain with example, the process building a decision tree.

2.

With reference to hierarchical clustering, explain the issue of connectivity constraints.

3.

Explain DBSCAN Clustering and Spectral Clustering?

4.

What are the building blocks of the deep networks, elaborate?

5.

With reference to clustering, explain the issue of finding the optimal number of clusters.

6.

What is deep learning? Explain the concept of fully connected layers.

7.

Explain different auto encoder components with example.

8.

What is mean by recommendation system? Explain architecture of recommendation system in detail.

9.

Short note on 1. Bagging 2. Boosting 3. Random Forest

10.

Elaborate Agglomerative clustering using sklearn library....