Machine Learning: Basics Exam Questions with
Answers
1. What is machine learning? correct answer: Machine learning is the field of
science that studies algorithms that approximate functions increasingly well as they are
given more observations.
2. What are some common applications of machine learning? correct
answer: Machine learning algo- rithms are often used to learn and automate human processes,
optimize outcomes, predict outcomes, model complex relationships, and to learn patterns in data (among
many other uses!)
3. What is labeled data and what is it used for? correct answer: Labeled data is
data that has the information
about target variable for each instance.
Labeled data allows us to train supervised machine learning algorithms.
4. What are the most common types of algorithms that use
supervised learn- ing? correct answer: The most common uses of supervised learning are
regression and classification.
5. What are the most common types of algorithms that use
unsupervised learn- ing? correct answer: The most common uses of unsupervised machine
learning are clustering, dimensionality reduction, and association-rule mining.
6. What is the difference between online and offline learning? correct
answer: Online learning refers to updating models incrementally as they gain more
information.
Offline learning refers to learning by batch processing data. If new data comes in, an entire new batch
(including all the old and new data) must be fed into the algorithm to learn from the new data.
7. What is reinforcement learning? correct answer: Reinforcement learning describes a
set of algorithms that learn
from the outcome of each decision. For example, a robot could use reinforcement learning to learn that
walking forward into a wall is bad, but turning away from a wall and walking is good.
8. What is the difference between a model parameter and a learning
hyperpa-
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, rameter? correct answer: A model parameter describes the final model itself, e.g. slope in a linear
model.
A learning hyperparameter describes the way in which a model parameter is learned, e.g. learning rate,
penalty terms, number of features to include in a weak predictor.
9. What is overfitting? correct answer: Overfitting when a model makes much better
predictions on known data (data
included in the training set) than unknown data (data not included in the training set).
10. How can you combat overfitting? correct answer: A few ways of combatting
overfitting are correct answer: simplify the model (often done by changing the
hyperparameters), select a ditterent model, use more training data, or gather better quality data to
combat overfitting.
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Answers
1. What is machine learning? correct answer: Machine learning is the field of
science that studies algorithms that approximate functions increasingly well as they are
given more observations.
2. What are some common applications of machine learning? correct
answer: Machine learning algo- rithms are often used to learn and automate human processes,
optimize outcomes, predict outcomes, model complex relationships, and to learn patterns in data (among
many other uses!)
3. What is labeled data and what is it used for? correct answer: Labeled data is
data that has the information
about target variable for each instance.
Labeled data allows us to train supervised machine learning algorithms.
4. What are the most common types of algorithms that use
supervised learn- ing? correct answer: The most common uses of supervised learning are
regression and classification.
5. What are the most common types of algorithms that use
unsupervised learn- ing? correct answer: The most common uses of unsupervised machine
learning are clustering, dimensionality reduction, and association-rule mining.
6. What is the difference between online and offline learning? correct
answer: Online learning refers to updating models incrementally as they gain more
information.
Offline learning refers to learning by batch processing data. If new data comes in, an entire new batch
(including all the old and new data) must be fed into the algorithm to learn from the new data.
7. What is reinforcement learning? correct answer: Reinforcement learning describes a
set of algorithms that learn
from the outcome of each decision. For example, a robot could use reinforcement learning to learn that
walking forward into a wall is bad, but turning away from a wall and walking is good.
8. What is the difference between a model parameter and a learning
hyperpa-
1/
13
, rameter? correct answer: A model parameter describes the final model itself, e.g. slope in a linear
model.
A learning hyperparameter describes the way in which a model parameter is learned, e.g. learning rate,
penalty terms, number of features to include in a weak predictor.
9. What is overfitting? correct answer: Overfitting when a model makes much better
predictions on known data (data
included in the training set) than unknown data (data not included in the training set).
10. How can you combat overfitting? correct answer: A few ways of combatting
overfitting are correct answer: simplify the model (often done by changing the
hyperparameters), select a ditterent model, use more training data, or gather better quality data to
combat overfitting.
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13
