Table of Contents
Question
You work for a large financial institution that wants to identify undervalued stocks. To do so, you feed decades of financial information into an artificial neural network to create clusters of stocks. Then your data science team tries to find stocks in those clusters that substantially increased in value. Your data science team hopes to find stocks in the same cluster that may also gain value.
What type of machine learning are you using?
A. generative artificial intelligence
B. supervised machine learning
C. unsupervised learning
D. reinforcement learning
Answer
C. unsupervised learning
Explanation
The answer to the question is C. unsupervised learning. Here is a detailed explanation:
Unsupervised learning is a type of machine learning that does not use any labelled data to train the model. Instead, it tries to find patterns or structures in the data by grouping similar data points together. This process is called clustering.
In the scenario, you are using an artificial neural network to create clusters of stocks based on decades of financial information. You are not telling the network what the correct output should be for each stock, but rather letting it discover the similarities and differences among them.
Then, you are looking for stocks in those clusters that have increased in value, hoping to find other potential candidates. This is an example of unsupervised learning, as you are not using any labels or feedback to guide the learning process.
Some other examples of unsupervised learning are anomaly detection, dimensionality reduction, and association rule mining.
The other options are incorrect because:
A. Generative artificial intelligence is not a type of machine learning, but rather a branch of AI that focuses on creating new content or data, such as images, text, music, etc. Generative models can use different types of machine learning techniques, such as supervised, unsupervised, or reinforcement learning.
B. Supervised machine learning is a type of machine learning that uses labelled data to train the model. It means that for each input, there is a corresponding output or target value that the model tries to learn and predict. Supervised learning can be used for tasks such as classification or regression.
D. Reinforcement learning is a type of machine learning that uses trial and error to learn from its own actions and rewards. It means that the model interacts with an environment and learns from the consequences of its actions, such as positive or negative feedback. Reinforcement learning can be used for tasks such as game playing or robot control.
Reference
- 3 Types of Machine Learning You Should Know | Coursera
- Types of Machine Learning – Javatpoint
- Types of Machine Learning | Simplilearn
- Machine learning – Wikipedia
- Python pipeline for finding undervalued stocks, using clustering | by Christopher Furu | Medium
- Microsoft Word – RehkiWardWei-FindingUndervaluedStocksWithMachineLearning.docx (stanford.edu)
- How To Find Undervalued Stocks In Malaysia (imoney.my)
- Machine learning, explained | MIT Sloan
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