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Artificial Intelligence and Machine Learning 2 Flashcards | StudBud
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Artificial Intelligence and Machine Learning 2
Artificial Intelligence and Machine Learning 2
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1
Which of these is a common application of artificial neural networks?
Image and speech recognition
Creating simple spreadsheets
Basic website design
Tracking inventory in small stores
2
Social media finding friend groups you didn't explicitly create is an example of:
Verification
Regression
Clustering
Classification
3
Which type of machine learning automatically groups similar items without predefined categories?
Reinforcement learning
Supervised learning
Binary learning
Unsupervised learning
4
Which of these is an example of unstructured data?
CSV Sheets
YouTube videos
Excel spreadsheets
Database tables
5
What does the "Value" V in the 5 V's of Big Data refer to?
The worth and usefulness of data for business purposes
The volume of data collected over time
The validity of the data collection method
The monetary cost of storing data
6
Which of these would be best solved using reinforcement learning?
Teaching a computer to play a video game
Categorizing emails as spam or not spam
Organizing a music library into genres
Predicting house prices based on features
7
Which of these best describes the difference between AI and machine learning?
AI requires human intervention, machine learning is autonomous
Machine learning is a subset of AI that learns from data
AI is real, machine learning is theoretical
AI is for business, machine learning is for science
8
What characteristic of big data refers to the different types and formats of data?
Visualization
Volume
Velocity
Variety
9
Which of these is NOT a typical real-world application of machine learning?
Predictive text on smartphones
Permanently storing all internet data
Facial recognition in photos
Voice assistants like Siri or Alexa
10
What is a potential ethical issue with machine learning?
It's always too expensive to implement
It requires too much electricity
It can perpetuate biases present in training data
It makes computers too hot
11
What is the key difference between regression and classification?
Regression requires more data than classification
Regression is supervised, classification is unsupervised
Regression works with images, classification works with text
Regression predicts continuous values, classification assigns categories
12
The process where machine learning algorithms improve automatically through experience is called:
Iterative development
Self-optimization
Automatic programming
Computational learning
13
What defines artificial neural networks?
They are inspired by the network of neurons in the human brain
They can only perform supervised learning
They always require internet connection
They only work with numerical data
14
Which of these is a limitation of tree-based models?
They can only use structured data
Small data changes can dramatically alter the entire tree
They can only handle text data
They require constant internet connection
15
A Waymo learning to navigate by receiving rewards for good decisions and penalties for mistakes is an example of:
Linear regression
Supervised learning
Reinforcement learning
Unsupervised learning
16
What type of machine learning output assigns items to specific categories?
Clustering
Association
Classification
Regression
17
Which of the following is NOT one of the three main machine learning methods?
Reinforcement learning
Supervised learning
Demonstrative learning
Unsupervised learning
18
What does "overfitting" mean in machine learning?
When AI systems become self-aware
When a model performs well on training data but poorly on new data
When too many users access an AI system at once
When a model is too large for available memory
19
What is a primary advantage of linear models?
They're easy to understand and interpret
They can handle extremely complex relationships
They always produce perfect predictions
They never require much data
20
In machine learning, what is a "training dataset"?
Exercise routines for data scientists
Data used to teach the algorithm patterns
Software that trains computers
Instructions for how to build neural networks
