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MACHINE LEARNING AND AI Flashcards | StudBud
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MACHINE LEARNING AND AI
MACHINE LEARNING AND AI
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Answers
1
Which of the following is a classification technique?
K-Nearest Neighbors (KNN)
Principal Component Analysis (PCA)
Linear Regression
Singular Value Decomposition (SVD)
2
It's important to evaluate a Machine Learning model's performance on the same data it was trained on to ensure accuracy.
False
True
3
The Naive Bayes algorithm assumes that all features are independent of each other, given the class label.
True
False
4
Which of the following is an example of a natural language processing (NLP) application?
Weather prediction
7. Satellite tracking
Image classification
Speech recognition
5
Naive Bayes is a classification algorithm based on:
Decision trees and random forests
Probability theory and Bayes' theorem
Gradient descent and optimization
Neural networks and deep learning
6
K-Nearest Neighbors (KNN) is primarily used for:
Image generation
Sentiment analysis
Clustering data
Making predictions based on similar cases
7
A linear regression model can be used to predict the price of a house based on its features.
False
True
8
What is the fundamental difference between supervised and unsupervised learning?
Unsupervised learning requires labeled data.
Unsupervised learning deals with structured data.
Supervised learning uses labeled examples for training.
Supervised learning doesn't involve modeling.
9
Artificial Intelligence (AI) is a broader concept that encompasses Machine Learning (ML), which is a subset of AI.
False
True
10
Machine Learning is not commonly used in fields like healthcare and finance to analyze data and make predictions.
True
False
11
What is the relationship between Artificial Intelligence (AI) and Machine Learning (ML)?
AI and ML are the same concepts.
AI is a subset of ML.
AI and ML are unrelated technologies.
ML is a subset of AI.
12
Descriptive models focus on predicting future outcomes based on historical data.
True
False
13
Supervised learning involves training a model with input-output pairs to learn a mapping function.
False
True
14
Inferential models are primarily used for:
Describing data distributions
Making predictions about new data
Presenting data visually
Summarizing data patterns
15
Reinforcement Learning is associated with:
Teaching machines to learn from labeled data
Training agents to make decisions based on rewards
Analyzing patterns in large datasets
Training models to analyze and interpret images
16
K-Nearest Neighbors (KNN) algorithm makes predictions based on weighted averages of nearest data points.
False
True
17
Machine Learning is primarily useful for tasks that have well-defined rules and straightforward algorithms.
True
False
18
Which of the following is a supervised learning technique?
Reinforcement Learning
K-means
Clustering
Decision Trees
19
What is the main goal of Machine Learning?
Creating robots with human-like intelligence
Programming computers without data
Improving computer hardware
Automating human tasks
20
What is the purpose of validation in Machine Learning?
To evaluate a model's performance on unseen data
To ensure that models memorize the data
To test models on the training data
To increase the complexity of the model
21
K-fold cross-validation involves splitting the dataset into K subsets and using each subset as the testing data once.
True
False
22
What is an algorithm in the context of Machine Learning?
A set of rules for computer hardware
A program that predicts future stock prices
A step-by-step recipe for cooking
A sequence of operations that solve a problem
23
Validation is the process of training a model on a large dataset and then testing it on a smaller dataset.
True
False
24
In which domain has Machine Learning NOT been widely applied?
Ancient History
Healthcare
Finance
Entertainment
25
What is the main goal of descriptive models in Machine Learning?
To make decisions based on actions
To summarize and interpret data
To explain causal relationships
To predict future events
26
In unsupervised learning, the model is provided with labeled examples for training.
False
True
27
What is a key advantage of using Machine Learning techniques?
It guarantees 100% accurate predictions
5. It is only applicable to scientific research
It eliminates the need for data
It can find patterns in large datasets
28
ML aims to develop algorithms that can improve their performance over time without being explicitly programmed.
True
False
29
Why is it important to split a dataset into training and testing sets in Machine Learning?
To increase the size of the training data
To ensure the model memorizes the data
To confuse the model and improve generalization
To evaluate the model's performance on unseen data
30
Classification is a type of Machine Learning task that involves predicting a continuous value.
True
False
