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WorksheetsIntroduction to Machine Learning and AI
Total questions: 55
Worksheet time: 18mins
What is the main goal of machine learning?
To follow fixed instructions
To learn patterns from data
To act randomly
To replace humans entirely
Which of the following is an example of supervised learning?
Clustering similar images
Predicting the weather using past data
Generating random text
Recognizing objects without labels
What is a "training dataset"?
A set of questions for the model
Data used to teach the model
Data to test the model's knowledge
A list of instructions
In machine learning, what does "overfitting" mean?
A model that performs poorly on training data
A model that learns too well from training data but fails on new data
A model that ignores all data
A model that is too simple
Which of these is NOT a type of machine learning?
Reinforcement learning
Supervised learning
Unsupervised learning
Predictive coding
What is the purpose of a chatbot?
To play games
To answer questions and have conversations
To store data
To create videos
Large language models like ChatGPT are trained using:
Only pictures
Text data from books, articles, and websites
Sounds from nature
Video clips
What does "token" mean in a large language model?
A type of currency
A piece of text, like a word or part of a word
A computer program
A chatbot's memory
How do chatbots generate answers?
By searching the internet every time
By guessing randomly
By using patterns learned during training
By asking other chatbots
What is one challenge for chatbots like ChatGPT?
Running out of memory
Making mistakes or giving incorrect answers
Refusing to respond
Being too fast
What is a neural network inspired by?
Stars in the sky
The human brain
A spider web
A road map
In a neural network, what is a "neuron"?
A piece of hardware
A small unit that processes information
A type of dataset
A programming language
What connects neurons in a neural network?
Cables
Wires
Weights
Data packets
What is the role of an "activation function" in a neural network?
To turn off the network
To decide whether a neuron sends a signal
To delete bad data
To store memory
Which is an example of a neural network task?
Calculating 2 + 2
Recognizing handwriting
Playing music
Saving files
What is "computer vision" used for?
Giving computers eyesight
Making computers understand images
Building cameras
Designing video games
What does "image classification" mean?
Sorting files on a computer
Identifying the category of an image
Taking pictures with a camera
Storing images in folders
What is "object detection"?
Finding and identifying objects in an image
Drawing pictures of objects
Removing objects from photos
Detecting errors in code
Which is an example of computer vision in real life?
Playing music
Facial recognition in phones
Sending emails
Calculating sums
What is a common challenge in computer vision?
Storing images
Understanding blurry or incomplete images
Drawing shapes
Translating languages
What does "AI" stand for?
Advanced Intelligence
Artificial Intelligence
Automated Intelligence
Augmented Intelligence
How does a model improve over time?
By guessing better
By learning from more data
By being reset frequently
By using faster computers
What is an example of reinforcement learning?
Training a dog to sit using rewards
Solving math problems step-by-step
Classifying emails as spam
Sorting colors in an image
What is "training" in AI?
Teaching the AI using examples
Making AI run faster
Testing the AI's knowledge
Fixing errors in code
What is "unsupervised learning"?
Learning without labels in the data
Learning with help from a teacher
Making random guesses
Using pre-written rules
Why do large language models need a lot of data?
To improve their vocabulary and understanding
To make them faster
To use less energy
To look impressive
What is the term for making computers understand spoken language?
Computer vision
Natural language processing
Data mining
Reinforcement learning
Which of these is an AI-powered tool?
A regular calculator
A handwriting recognition app
A traditional watch
A vacuum cleaner without sensors
How do neural networks "learn"?
By building new connections and adjusting weights
By memorizing data
By copying humans
By guessing repeatedly
What is the main input for a computer vision model?
Text data
Image or video data
Sound files
Numbers only
Machine learning is a branch of AI where machines learn from _____ without being explicitly programmed.
Sensors
Data
Instructions
Hardware
The goal of AI is to create machines that can perform tasks that typically require _____ intelligence.
Physical
Animal
Human
Natural
In supervised learning, the AI is trained using _____ data, which has inputs and corresponding outputs.
Random
Labeled
Unorganized
Empty
The process of improving the performance of a machine learning model is called _____.
Training
Building
Tuning
Debugging
A system that uses machine learning to improve as it processes more data is said to be _____.
Fixed
Intelligent
Adaptive
Limited
Large Language Models are trained on massive amounts of _____ to generate human-like responses.
Text
Images
Code
Numbers
A chatbot understands user input using natural language _____.
Processing
Typing
Commands
Training
Chatbots use _____ to predict the best possible response to a user's question.
Random guesses
Pre-programmed scripts
Machine learning algorithms
Internet searches
The training process for chatbots often involves input-output pairs, also known as _____.
Dialogues
Instructions
Datasets
Maps
Chatbots like ChatGPT are examples of _____ AI because they are designed for specific tasks.
General
Narrow
Advanced
Experimental
The basic unit of a neural network is called a _____.
Node
Cell
Core
Loop
The connection between two nodes in a neural network is called a _____.
String
Chain
Weight
Path
Neural networks adjust their weights during training to reduce the _____.
Error
Input
Speed
Output
The type of learning where a neural network learns without labeled data is called _____.
Supervised
Unsupervised
Reinforcement
Assisted
Deep learning refers to neural networks with multiple _____ that allow them to learn complex patterns.
Outputs
Layers
Connections
Inputs
What is the first step in building a machine learning model?
Training the model
Collecting and preparing data
Testing the model
Writing the final report
What is a "feature" in machine learning?
A special ability of a model
An input variable that helps the model make predictions
A type of AI algorithm
A mistake in the code
When splitting data for building a model, what is the test set used for?
Checking how well the model performs on new data
Improving the training data
Training the model
Storing unused data
Why is it important to clean data before training a model?
To make the code shorter
To make it colorful
To make it harder for the model to learn
To remove errors or irrelevant information
What does "training" a machine learning model mean?
Showing the model examples so it can learn patterns
Writing code for the model
Fixing errors in the data
Testing the model on new data
When would you use a neural network over a simple algorithm?
When data is small and simple
When the problem has many complex patterns or layers
When you want faster results with fewer resources
When only numbers are involved
What is a pre-trained model?
A model with errors in it
A type of hardware used in AI
A model without any data
A model that has already been trained on data and can be reused
What does "accuracy" measure in a model?
How fast the model runs
How many predictions are correct
How well the data is cleaned
How simple the model is
Why is it important to test your model on new data (test data)?
To confuse the model
To make the model slower
To see if it can memorize data
To check if it works well with unseen data
How can you improve a poorly performing model?
Use more data for training
Try a different algorithm
Fine-tune hyperparameters
All of the above
