Machine Learning-based Multiple choice questions

1. ML is a field of AI consisting of learning algorithms that?

A. Improve their performance
B. At executing some task
C. Over time with experience
D. All of the above

2. The action _______ of a robot arm specifies to Place block A on block B.

A. STACK(A, B)
B. LIST(A, B)
C. QUEUE(A, B)
D. ARRAY(A, B)

3.A model of language consists of the categories which do not include ________.

A. System Unit
B. structural units.
C. data units
D. empirical units

4. Different learning methods do not include?

A. Introduction
B. Analogy
C. Deduction
D. Memorization



5. The model will be trained with data in one single batch is known as?

A. Batch learning
B. Offline learning
C. Both A and B
D. None of the above

6. Which of the following are ML methods?

A. based on human supervision
B. supervised Learning
C. semi-reinforcement Learning
D. All of the above



7. In Model-based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called?

A. mini-batches
B. optimized parameters
C. hyperparameters
D. super parameters

8. What is Machine Learning (ML)?

A. The autonomous acquisition of knowledge through the use of manual programs
B. The selective acquisition of knowledge through the use of computer programs
C.The selective acquisition of knowledge through the use of manual programs
D.The autonomous acquisition of knowledge through the use of computer programs

9. In general, to have a well-defined learning problem, we must identify which of the following

A. The class of tasks
B. The measure of performance to be improved
C. The source of experience
D. All of the above

10. Successful applications of ML?

A. Learning to recognize spoken words
B. Learning to drive an autonomous vehicle
C. Learning to classify new astronomical structures
D. Learning to play world-class backgammon
E. All of the above

11. In language understanding, the levels of knowledge that do not include?

A. Empirical
B. Logical
C. Phonological
D. Syntactic

12. Which of the following is not supervised learning?

A. Naive Bayesian
B. PCA
C. Linear Regression
D. Decision Tree Answer

13. What kind of learning algorithm for “Facial identities or facial expressions”?

A. Prediction
B. Recognition Patterns
C. Generating Patterns
D. Recognizing Anomalies Answer

14. Which of the following is not a type of learning?

A. Unsupervised Learning
B. Supervised Learning
C. Semi-unsupervised Learning
D. Reinforcement Learning

15. Real-Time decisions, Game AI, Learning Tasks, Skill Acquisition, and Robot Navigation are applications of which of the following?

A. Supervised Learning: Classification
B. Reinforcement Learning
C. Unsupervised Learning: Clustering
D. Unsupervised Learning: Regression

16. Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in which of the following

A. Unsupervised Learning: Regression

B. Supervised Learning: Classification

C. Unsupervised Learning: Clustering

D. Reinforcement Learning

17. Inductive learning is based on the knowledge that if something happens a lot it is likely to be generally?

True
False

   
                            Decision Trees

1. Which one is the most important supervised algorithm

A. K-nears neighbors

B.  Linear Regression and Logistic Regression

C. Neural networks

D. Support Vector Machine

E.  Decision Tree and Random forest

F.  All of the above

2. Which one is the most important unsupervised algorithms

 A. clustering: k-mean, hierarchical cluster analysis

 B. Association Rule Learning: Eclat, apriori

 C.Kernel PCA & PCA

 D.t-Distributed

 E. All of the above

3. Concept learning inferred a _____valued function from training examples of its input and output. 

A.  Decimal 

B. Boolean 

C. Hexadecimal 

D.  All of the above

4. For each attribute, the hypothesis indicates by a "?' means 

 A. That no value is acceptable 

 B. That any value is acceptable for this attribute 

 C. Specify a single required value for the attribute 

 D.  None of the above