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Artificial Intelligence and Machine Learning 1 Flashcards | StudBud
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Artificial Intelligence and Machine Learning 1
Artificial Intelligence and Machine Learning 1
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Answers
1
Which of the following is the "how much?" or "how many?" predictor
Verification
Regression
Clustering
Classification
2
What type of learning occurs when a computer is trained with labeled examples?
Supervised learning
Reinforcement learning
Cognitive learning
Unsupervised learning
3
Spam filters deciding if an email is junk or important is an example of:
Clustering
Structuring
Classification
Regression
4
Which of these is an advantage of tree-based models?
They work well with unstructured data
They are visually easy to understand
They can predict human emotions
They require minimal computing power
5
What type of data is a spreadsheet an example of?
Operational logging data
Unstructured data
Structured data
Semi-structured data
6
Which of these is NOT one of the three main machine learning algorithm approaches?
Linear Model Approach
Tree-Based Model Approach
Database Query Approach
Artificial Neural Networks
7
Which of the 5 V's of Big Data refers to how quickly new data is created and processed?
Veracity
Volume
Velocity
Variety
8
Which of these is NOT a common limitation of machine learning systems?
Data bias issues
Social acceptance challenges
Unlimited processing power
Hardware limitations
9
What type of machine learning output predicts numerical values?
Regression
Classification
Verification
Clustering
10
What is Machine Learning?
The ability of computers to mimic human emotions
The process of building physical robots
AI systems that learn from data without explicit programming
Computer systems designed only for data storage
11
Which type of machine learning is most similar to how humans learn to ride a bicycle?
Unsupervised learning
Linear learning
Supervised learning
Reinforcement learning
12
The "V" in the 5 V's that refers to the accuracy and trustworthiness of data is:
Value
Validity
Veracity
Verification
13
What is the main limitation of linear models?
They require internet connection to function
They're too simple for complex relationships
They use too much computing power
They can only work with text data
14
What happens in overfitting?
The computer overheats from processing
The model is too large for the computer's memory
The model memorizes examples instead of learning concepts
The data becomes too complex to process
15
Which learning method finds patterns without being given the answers first?
Reinforcement learning
Unsupervised learning
Structured learning
Supervised learning
16
TikTok's algorithm showing you content you might enjoy is an example of:
Reinforcement punishment
Overfitting problem
Supervised learning application
Hardware limitation
17
What is the main difference between supervised and unsupervised learning?
Supervised learning uses labeled examples, unsupervised doesn't
Supervised learning requires human oversight, unsupervised doesn't
Supervised learning is for images, unsupervised is for text
Supervised learning is faster, unsupervised is slower
18
Which machine learning approach is inspired by the human brain?
Tree-Based Model Approach
Sequential Learning Models
Linear Model Approach
Artificial Neural Networks
19
Netflix grouping similar viewers to recommend new shows is an example of:
Regression
Clustering
Classification
Linear modeling
20
When teaching computers to recognize things, it's important to show them:
As few examples as possible
Only positive examples
Both what to look for AND what to ignore
Only negative examples
