Machine Learning 1: UNIT 1 The Machine Learning Landscape Questions

 UNIT-1

The Machine Learning Landscape

Short Answer Questions

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1.     Define Machine Learning.

2.     Define Unsupervised Learning.

3.     Explain Supervised Learning Algorithm.

4.     List the different Machine Learning Challenges.

5.     What is the purpose of the Machine Learning.

6.     List the different Machine Learning Applications.

7.     Define Reinforcement Learning.

8.     Define batch learning

9.     Define online learning

10.  List the different machine learning systems

11.  Machine learning types.

12.  List the supervised learning algorithms

13.  List the unsupervised learning algorithms

14.  What is overfitting

15.  What is underfitting

16.  What is poor quality data

17.  What is irrelevant feature

18.  Insufficient quantity of training data

19.  Non representative training data

20.  Testing

21.  Validation

22.  Types of machine learning systems

 

UNIT- I

Long Answer Questions

1.     Compare and contrast between supervised and unsupervised learning algorithms with examples.

2.     Define Underfitting the Training Data. How can overcome Underfitting the Training Data? Explain.

3.     Explain how Model -based Learning Algorithms differ from Instance Based Learning with example.

4.      What is Overfitting of Training Data. How can you overcome the Overfitting of Training Data?

5.     Why Use Machine Learning?

6.     Explain Batch and Online Learning with example.

7.      Explain about Instance-Based Versus Model-Based Learning .

8.     List the different types of Machine Learning Systems. Explain them.

9.     Define Machine Learning. Give different applications of Machine Learning.

10.  List the Challenges of Machine Learning. Explain them in detail.

11.   What is Underfitting of Training Data. How can you overcome the underfitting of Training Data?

12.  Discuss the following

a.      Insufficient Quantity of Training Data

b.      Nonrepresentative Training Data

c.      Poor-Quality Data

13.  Discuss the following

a.      Irrelevant Features

b.      Overfitting the Training Data

c.      Underfitting the Training Data