Font size
WorksheetsMachine Learning
Total questions: 20
Worksheet time: 10mins
What are the two types of Unsupervised Learning? Choose two.
Regression
Clustering
Association
Classification
What are the two types of Supervised Learning? Choose two.
Classification
Declassification
Progression
Regression
What are the three types of Machine Learning? Choose three.
Supervised Learning
Differentiated Learning
Unsupervised Learning
Reinforcement Learning
Technical Learning
What is Machine learning?
The selective acquisition of knowledge through the use of computer programs
The selective acquisition of knowledge through the use of manual programs
The autonomous acquisition of knowledge through the use of computer programs
The autonomous acquisition of knowledge through the use of manual programs
Which of the following is not a supervised machine learning algorithm?
K-means
Naïve Bayes
SVM for classification problems
Decision tree
Which algorithm is best suited for a binary classification problem?
K-nearest Neighbors
Decision Trees
Random Forest
Linear Regression
What is the key difference between supervised and unsupervised learning?
Supervised learning requires labeled data, while unsupervised learning does not.
Supervised learning predicts labels, while unsupervised learning discovers patterns.
Supervised learning is used for classification, while unsupervised learning is used for regression.
Supervised learning is always more accurate than unsupervised learning.
An AI-based car automatically slows down as it gets closer to the car in front. Which algorithm is responsible for this behavior?
Naïve-Bayes
Decision Tree
Linear Regression
Logistic Regression
In the context of Industry 4.0, what is a key role of Machine Learning in manufacturing?
Designing new mechanical parts
Predicting equipment failure before it happens
Operating CNC machines manually
Reducing workforce requirement
Which Machine Learning task is most relevant to identifying defective parts on a production line using image data?
Clustering
Classification
Regression
Reinforcement
In smart factories, sensor data is collected from machines in real time. If the data is labeled as normal or faulty, what type of Machine Learning is commonly applied to analyze this data for fault detection?
Unsupervised Learning
Supervised Learning
Reinforcement Learning
Evolutionary Learning
Which of the following is an example of predictive maintenance using Machine Learning?
Shutting down a machine when it overheats
Replacing all parts after 6 months
Using vibration patterns to forecast bearing failure
Lubricating parts daily
Which type of machine learning model is ideal for learning patterns from large volumes of unlabeled sensor data in manufacturing?
Supervised models
Regression trees
Unsupervised models
Decision trees
In Industry 4.0, what does the term “Digital Twin” mean, and how is ML related to it?
Two robots working in parallel
A virtual copy of a physical system trained using real-time data
Two PLCs mirroring each other
A cloned software for industrial robots
What kind of Machine Learning is used to group similar types of manufactured items based on sensor measurements without prior labels?
Classification
Clustering
Regression
Supervised learning
In a production environment, which ML technique would help adjust machine parameters automatically to optimize quality and speed?
Logistic Regression
Reinforcement Learning
K-Means Clustering
Naïve Bayes
In the context of Industry 4.0, how does the Internet of Things (IoT) contribute to manufacturing efficiency?
By enabling real-time data collection and analysis
By replacing all manual processes
By increasing the physical size of machines
By eliminating the need for software updates
Which type of machine learning is primarily used for predicting continuous outcomes based on input features?
Reinforcement Learning
Classification
Regression
Clustering
What is the primary goal of unsupervised learning in data analysis?
To classify data into predefined categories
To find hidden patterns in data
To optimize decision-making processes
To predict future outcomes
In the context of Machine Learning, what does the term 'overfitting' refer to?
A model that requires more data to improve accuracy
A model that generalizes well to new data
A model that performs well on training data but poorly on unseen data
A model that is too simple to capture the underlying patterns
