Machine Learning Technologies Exam Questions
with Answers
1. How would you define Machine Learning? correct answer: Machine Learning is
about building systems that can learn from data. Learning means getting better at
some task, given some performance measure.
2. Can you name four types of problems where machine learning
shines? correct answer: Machine Learning is great for complex problems for which we
have no algorithmic solution, to
replace long lists of hand-tuned rules, to build systems that adapt to fluctuating
environments, and finally to help humans learn (e.g., data mining).
3. What is a labeled training set? correct answer: A labeled training set is a training set
that contains the desired solution (a.k.a. a label) for each
instance.
4. What are the two most common supervised tasks? correct answer: The two
most common supervised tasks are regression and classification.
5. Can you name four common unsupervised tasks? correct answer: Common
unsupervised tasks include clustering, visualization, dimensionality reduction, and
association rule learning.
6. What type of Machine Learning algorithm would you use to allow a
robot to walk in various
unknown terrains? correct answer: Reinforcement Learning is likely to perform best if we want a
robot to learn to walk in various
unknown terrains since this is typically the type of problem that Reinforcement Learning
tackles. It might be possible to express the problem as a supervised or semisupervised
learning problem, but it would be less natural.
7. What type of algorithm would you use to segment your
customers into multiple groups? correct answer: If you don't know how to define the
groups, then you can use a clustering algorithm (unsupervised
learning) to segment your customers into clusters of similar customers. However, if you
know what groups you would like to have, then you can feed many examples of each
group to a classification algorithm (supervised learning), and it will classify all your
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5
, customers into these groups.
8. Would you frame the problem of spam detection as a supervised
learning problem or an
unsupervised learning problem? correct answer: Spam detection is a typical supervised
learning problem correct answer: the algo-
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5
with Answers
1. How would you define Machine Learning? correct answer: Machine Learning is
about building systems that can learn from data. Learning means getting better at
some task, given some performance measure.
2. Can you name four types of problems where machine learning
shines? correct answer: Machine Learning is great for complex problems for which we
have no algorithmic solution, to
replace long lists of hand-tuned rules, to build systems that adapt to fluctuating
environments, and finally to help humans learn (e.g., data mining).
3. What is a labeled training set? correct answer: A labeled training set is a training set
that contains the desired solution (a.k.a. a label) for each
instance.
4. What are the two most common supervised tasks? correct answer: The two
most common supervised tasks are regression and classification.
5. Can you name four common unsupervised tasks? correct answer: Common
unsupervised tasks include clustering, visualization, dimensionality reduction, and
association rule learning.
6. What type of Machine Learning algorithm would you use to allow a
robot to walk in various
unknown terrains? correct answer: Reinforcement Learning is likely to perform best if we want a
robot to learn to walk in various
unknown terrains since this is typically the type of problem that Reinforcement Learning
tackles. It might be possible to express the problem as a supervised or semisupervised
learning problem, but it would be less natural.
7. What type of algorithm would you use to segment your
customers into multiple groups? correct answer: If you don't know how to define the
groups, then you can use a clustering algorithm (unsupervised
learning) to segment your customers into clusters of similar customers. However, if you
know what groups you would like to have, then you can feed many examples of each
group to a classification algorithm (supervised learning), and it will classify all your
1/
5
, customers into these groups.
8. Would you frame the problem of spam detection as a supervised
learning problem or an
unsupervised learning problem? correct answer: Spam detection is a typical supervised
learning problem correct answer: the algo-
2/
5
