Question
Question: 1
Identify which type of machine learning (ML) best describes the scenario below:
A company develops an ML system that displays ads targeted to each user on their social media platform based on the user’s activity data.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 2
Identify which type of machine learning (ML) best describes the scenario below:
A gardening app uses thousands of images of flowers to train its model to detect and predict the flowers appearing in a user-uploaded photo. The training images were submitted by users over time and each included the name of the flower in the image.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 3
Identify which type of machine learning (ML) best describes the scenario below:
A music company has created a website that allows users to say if they like or dislike a song that they are listening to. Each song has a list of traits, like tempo, instrument types, vocals, etc. Using the like/dislike data submitted by the user, the website then makes predictions on whether or not they will like certain new songs, ultimately playing the songs it thinks the user will like.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 4
Identify which type of machine learning (ML) best describes the scenario below:
A new Tetris video game wants to include computer players that the human players can play against. They allow their ML system to play the game thousands of times, giving it point rewards every time it successfully clears the lines and survives another 30 seconds. Once trained, the computer player can beat many beginner and intermediate players!
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 5
Identify which type of machine learning (ML) best describes the scenario below:
In order to boost sales, an online retail website will randomly suggest new products to a shopper every 10 minutes they spend on the site. The shopper can then choose if they want to add the product to their shopping cart or not.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 6
Identify which type of machine learning (ML) best describes the scenario below:
A movie streaming website generates a list of recommended movies for each user based on the watching activity of all of its users. Its extremely successful because its ML system is able to identify patterns between commonly linked movies. In other words, it knows that if one user watches movie A all the way to the end, it is highly probably that it will also watch movie B all the way to the end.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Identify which type of machine learning (ML) best describes the scenario below:
A company develops an ML system that displays ads targeted to each user on their social media platform based on the user’s activity data.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 2
Identify which type of machine learning (ML) best describes the scenario below:
A gardening app uses thousands of images of flowers to train its model to detect and predict the flowers appearing in a user-uploaded photo. The training images were submitted by users over time and each included the name of the flower in the image.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 3
Identify which type of machine learning (ML) best describes the scenario below:
A music company has created a website that allows users to say if they like or dislike a song that they are listening to. Each song has a list of traits, like tempo, instrument types, vocals, etc. Using the like/dislike data submitted by the user, the website then makes predictions on whether or not they will like certain new songs, ultimately playing the songs it thinks the user will like.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 4
Identify which type of machine learning (ML) best describes the scenario below:
A new Tetris video game wants to include computer players that the human players can play against. They allow their ML system to play the game thousands of times, giving it point rewards every time it successfully clears the lines and survives another 30 seconds. Once trained, the computer player can beat many beginner and intermediate players!
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 5
Identify which type of machine learning (ML) best describes the scenario below:
In order to boost sales, an online retail website will randomly suggest new products to a shopper every 10 minutes they spend on the site. The shopper can then choose if they want to add the product to their shopping cart or not.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Question: 6
Identify which type of machine learning (ML) best describes the scenario below:
A movie streaming website generates a list of recommended movies for each user based on the watching activity of all of its users. Its extremely successful because its ML system is able to identify patterns between commonly linked movies. In other words, it knows that if one user watches movie A all the way to the end, it is highly probably that it will also watch movie B all the way to the end.
Supervised Learning
Unsupervised Learning
Reinforcement Learning
None of the above
Answers
GPT-4o mini
Here are the answers to the questions based on the types of machine learning described in each scenario:
**Question 1**: A company develops an ML system that displays ads targeted to each user based on the user’s activity data.
- **Answer**: **None of the above** (This is more of a recommendation system, which can involve elements of supervised, unsupervised, or reinforcement learning but does not fit neatly into one category.)
**Question 2**: A gardening app uses thousands of images of flowers to train its model to detect and predict the flowers in a user-uploaded photo, with training images labeled with flower names.
- **Answer**: **Supervised Learning** (The model is trained using labeled data where each image has a corresponding flower name.)
**Question 3**: A music company allows users to indicate if they like or dislike a song, then predicts new songs the user might like based on this feedback.
- **Answer**: **Supervised Learning** (The model uses labeled data from user feedback (like/dislike) to make predictions about new songs.)
**Question 4**: A new Tetris video game uses an ML system that receives point rewards for successfully clearing lines and surviving for time, allowing it to learn to play better.
- **Answer**: **Reinforcement Learning** (The model learns through rewards for its actions based on the outcomes it experiences in the game.)
**Question 5**: An online retail website suggests new products randomly to shoppers, who can choose to add them to their shopping cart.
- **Answer**: **None of the above** (This is more random recommendation and does not fit neatly into supervised, unsupervised, or reinforcement learning.)
**Question 6**: A movie streaming site generates recommendations based on patterns in the watching activity of users, predicting what users might watch next.
- **Answer**: **Unsupervised Learning** (The system identifies patterns and associations in user behavior without labeled output for training.)
Overall, the scenarios cover different applications of machine learning, each fitting into specific categories based on the data used and the learning approach.
**Question 1**: A company develops an ML system that displays ads targeted to each user based on the user’s activity data.
- **Answer**: **None of the above** (This is more of a recommendation system, which can involve elements of supervised, unsupervised, or reinforcement learning but does not fit neatly into one category.)
**Question 2**: A gardening app uses thousands of images of flowers to train its model to detect and predict the flowers in a user-uploaded photo, with training images labeled with flower names.
- **Answer**: **Supervised Learning** (The model is trained using labeled data where each image has a corresponding flower name.)
**Question 3**: A music company allows users to indicate if they like or dislike a song, then predicts new songs the user might like based on this feedback.
- **Answer**: **Supervised Learning** (The model uses labeled data from user feedback (like/dislike) to make predictions about new songs.)
**Question 4**: A new Tetris video game uses an ML system that receives point rewards for successfully clearing lines and surviving for time, allowing it to learn to play better.
- **Answer**: **Reinforcement Learning** (The model learns through rewards for its actions based on the outcomes it experiences in the game.)
**Question 5**: An online retail website suggests new products randomly to shoppers, who can choose to add them to their shopping cart.
- **Answer**: **None of the above** (This is more random recommendation and does not fit neatly into supervised, unsupervised, or reinforcement learning.)
**Question 6**: A movie streaming site generates recommendations based on patterns in the watching activity of users, predicting what users might watch next.
- **Answer**: **Unsupervised Learning** (The system identifies patterns and associations in user behavior without labeled output for training.)
Overall, the scenarios cover different applications of machine learning, each fitting into specific categories based on the data used and the learning approach.