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