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WorksheetsMachine Learning Introduction and Basics
Total questions: 25
Worksheet time: 8mins
__________________ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.
Artificial Intelligence
Machine Learning
Deep Learning
Traditional Learning
_______________________ is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.
Artificial Intelligence
Machine Learning
Deep Learning
Traditional Learning
What device below is not an example of Machine Learning?
Wearable fitness tracker
Google Assistant
Speech to Text
Google Search
None of the above
Check/Click all which uses Machine Learning below.
Prediction
Image Recognition
Face Recognition
Medical Diagnoses
Feeding the newborn
What year was the E.N.I.A.C. first invented?
1904
1914
1940
1490
'6. What year did Artifical Intelligence first stirred excitement?
1905
1950
1590
1915
What year did Machine Learning began to flourish?
1980
1918
1908
1890
What year did Deep Learning breakthroughs drove A.I. (Artificial Intelligence) boom?
2010
2009
2001
2008
What doe ENIAC stands for?
Electric Number Intersect A Calculator
Electronic Numerical Integrator and Computer
Engineering Numbering Into Auto Correct
Enter Number In Auto Correct
Who invented the Perceptron which was a very, very simple classifier but when it was combined in large numbers, in a network, it became a powerful monster?
Frank Rosemarie
Frank Rosenblet
Frank Rosenblatt
Frank Rosenbat
I.B.M.’s _______________ system beat the world champion of chess, the grand-master.
Deep Sea
Deep Ocean
Deep Lake
Deep Blue
Who is the Chess grand master beaten in a game by I.B.M.'s system?
Gary Kapov
Garry Kasper
Gary Kasparov
Gary Kerpov
What are the three types of Machine Learning? Choose three.
Supervised Learning
Learning Differentiated
Unsupervised Learning
Reinforcement Learning
Technical Learning
What are the two types of Supervised Learning?
Classification
Declassification
Progression
Regression
What are the two types of Unsupervised Learning?
Loitering
Clustering
Association
Dissociation
This type of Machine Learning learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty. It is a type of dynamic programming that trains algorithms using a system of reward and punishment.
Supervised Learning
Unsupervised Learning
Learning and Teaching
Reinforcement Learning
In this type of Machine Learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Technique Learning
In this kind of Machine Learning, an AI system is presented with data which is labeled, which means that each data tagged with the correct label.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Learning and Techniques
What is "ML"?
Maternity Leave
Mixed Language
Missing Link
Machine Language
Tom Mitchell of Carnegie Mellon University said that, "A computer program is said to learn from experience E with respect to some "T" and some performance measure P, if its performance on T, as measured by P, improves with experience E." What is "T"?
Time
Test
Task
Temper
What is Machine Learning? (Choose 3 Answers)
Artificial Intelligence
Machine Learning
Data Statistics
Deep Learning
Which one in the following is not Machine Learning disciplines?
Information Theory
Neurostatistics
Optimization + Control
Physics
Which of the following is not type of learning?
Semi-unsupervised Learning
Unsupervised Learning
Supervised Learning
Reinforcement Learning
This picture shows a result of ...
Supervised Learning: Classification
Unsupervised Learning: Regression
Unsupervised Learning: Prediction
Supervised Learning: Regression
This picture shows an application of ...
Supervised Learning: Classification
Unsupervised Learning: Clustering
Unsupervised Learning: Prediction
Supervised Learning: Regression
