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iSQI CT-AI Exam - Topic 2 Question 6 Discussion

Actual exam question for iSQI's CT-AI exam
Question #: 6
Topic #: 2
[All CT-AI Questions]

A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?

SELECT ONE OPTION

Show Suggested Answer Hide Answer
Suggested Answer: C

Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:

Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).

Why Not Other Options:

Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.

Regression: This is used for predicting continuous values, not discrete categories like digit recognition.

Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.


Contribute your Thoughts:

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Darrin
3 months ago
Classification makes the most sense here, no doubt!
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Belen
3 months ago
I thought clustering was the way to go for this.
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Jacob
3 months ago
Wait, isn’t it more like regression?
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Audry
4 months ago
I agree, it’s all about categorizing the digits.
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Gussie
4 months ago
This is definitely classification!
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Raymon
4 months ago
I practiced a similar question, and I think classification is the best fit for recognizing handwritten digits.
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Janella
4 months ago
Clustering seems off too, since we’re not grouping similar items but rather categorizing them into distinct classes.
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Jeniffer
4 months ago
I'm not entirely sure, but I remember something about regression being used for predicting values, so that doesn't seem right here.
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Naomi
5 months ago
I think this might be classification since we're trying to identify specific digits from the images.
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Joseph
5 months ago
Okay, let's see. The question says the machine learning is used to "recognize" the digits, which makes me think it's a classification task. I'll go with option C.
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Tresa
5 months ago
Hmm, I'm a bit unsure here. Classifying digits could also be considered a regression problem, since we're trying to map the input image to a numerical output. I'll have to think this through carefully.
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Blair
5 months ago
This seems like a straightforward classification problem to me. The machine learning model is trying to classify the handwritten digits, so I'll go with option C.
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Kenny
5 months ago
I'm a little confused by this question. Reinforcement learning, regression, and clustering all seem like possible answers. I'll have to review my notes on the different machine learning techniques to decide.
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Sherron
5 months ago
Hmm, I'm a bit confused by the details here. I'll need to re-read the question and really understand the relationship between the purchase order, sales order, and intercompany project. Let me think this through step-by-step.
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Elouise
5 months ago
I remember we talked about capitation models in class, but I'm not 100% sure which one this is.
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Jamey
5 months ago
I'm a bit confused on this one. I know the extract process is important, but the other two options have me stumped. I'll have to make an educated guess and hope for the best.
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Charlena
5 months ago
Hmm, I'm not sure about this one. The question is a bit confusing. I'll need to think it through carefully.
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Wendell
2 years ago
Hey, at least it's not D) Clustering. That would be a real mess, trying to group handwritten digits together!
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Tandra
2 years ago
C) Classification
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Buffy
2 years ago
C) Classification
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Reynalda
2 years ago
B) Regression
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Jonell
2 years ago
A) Reinforcement learning
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Suzan
2 years ago
Recognizing digits, huh? Sounds like a job for my trusty neural network. I bet C) Classification is the way to go.
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Mitsue
1 year ago
I would go with C) Classification as well. It seems like the most suitable option for recognizing handwritten numbers.
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Katina
1 year ago
Yeah, I agree. Classification is definitely the way to go for this scenario.
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Minna
1 year ago
I think you're right, C) Classification is the best choice for recognizing digits.
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Barney
2 years ago
It makes sense, since we're trying to classify the scanned handwritten numbers into specific digits.
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Sarah
2 years ago
Yeah, I agree. Classification is typically used for categorizing data into different classes.
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Casandra
2 years ago
I think you're right, C) Classification seems like the best fit for recognizing digits.
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Glendora
2 years ago
Hmm, I'm not sure if it's B) Regression, since we're not trying to predict a continuous value. C) Classification seems more appropriate here.
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Maile
2 years ago
Yeah, I agree. We're categorizing the digits into different classes, so it's definitely classification.
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Dorinda
2 years ago
I think you're right, C) Classification makes more sense for recognizing digits.
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Yuonne
2 years ago
I would go with C) Classification too, as it involves categorizing data into different classes.
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Tasia
2 years ago
I agree with Wilburn, because it's about recognizing specific categories.
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Shawnta
2 years ago
Recognizing digits from a handwritten scan? Definitely sounds like a classification problem to me.
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Gussie
2 years ago
This looks like a classic case of image classification using machine learning. I'd say the correct answer is C) Classification.
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Willard
2 years ago
Yeah, C) Classification seems like the most fitting option for this scenario.
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Antonio
2 years ago
I would go with C) Classification as well, since it's about recognizing specific patterns.
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Laurel
2 years ago
I think it's more about categorizing the digits, so C) Classification makes sense.
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Haley
2 years ago
Yeah, I think so too. C) Classification makes the most sense in this scenario.
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Buddy
2 years ago
I agree, it's definitely image classification. C) Classification is the right choice.
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Adell
2 years ago
I agree, it's definitely image classification. C) Classification is the right choice.
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Wilburn
2 years ago
I think it's C) Classification.
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