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AI & Machine Learning Basics Quiz

Q1. What does “AI” stand for?

Artificial Intelligence
Automated Integration
Applied Innovation
Algorithmic Index

Explanation: AI refers to machines designed to mimic human intelligence.

Q2. Which of the following is an example of supervised learning?

K-Means Clustering
Linear Regression
Principal Component Analysis
DBSCAN

Explanation: Linear regression is a supervised learning algorithm because it learns from labeled data.

Q3. What is overfitting in machine learning?

When the model is too simple
When the model performs well on training data but poorly on new data
When the data is corrupted
When the algorithm runs too slowly

Explanation: Overfitting occurs when the model learns noise from the training data instead of general patterns.

Q4. Which of these is a common activation function in neural networks?

Mean Squared Error
Gradient Descent
ReLU
Random Forest

Explanation: ReLU (Rectified Linear Unit) is widely used in neural networks as an activation function.

Q5. What is the difference between AI and ML?

ML is a subset of AI focused on learning from data
AI is a subset of ML
AI and ML are the same
ML refers to hardware, AI to software

Reference: Intro to AI & ML

I’ll analyze the existing quiz and provide two additional questions while maintaining the same format:

Q6. What is Deep Learning?

A subset of machine learning using neural networks with multiple layers
A type of database management system
A programming language for AI
A method of storing large datasets

Explanation: Deep Learning is a specialized subset of machine learning that uses neural networks with multiple (deep) layers to progressively extract higher-level features from raw input. It’s particularly powerful for complex tasks like image and speech recognition.

Source: Deep Learning - MIT Press

Q7. Which of these is NOT a common type of machine learning?

Supervised Learning
Unsupervised Learning
Reinforcement Learning
Perpetual Learning

Explanation: While Supervised, Unsupervised, and Reinforcement Learning are the three main types of machine learning, “Perpetual Learning” is not a standard classification. The main categories are:

Supervised Learning (learns from labeled data)
Unsupervised Learning (finds patterns in unlabeled data)
Reinforcement Learning (learns through trial and error with rewards/penalties)

Source: Types of Machine Learning - Stanford University CS229

Q8. What is the main goal of reinforcement learning?

To learn from labeled data
To reduce dimensionality of data
To learn optimal actions through rewards and penalties
To predict continuous values

Explanation: RL agents learn to take actions that maximize cumulative reward through feedback loops.

Reference: Reinforcement Learning - Sutton & Barto

Q9. Which algorithm is commonly used for classification tasks?

Decision Tree
K-Means
Apriori
DBSCAN

Explanation: Decision Trees are popular for classification because they split data based on feature values to predict labels.

Reference: Decision Tree Classifier - Scikit-learn

Q10. What is the purpose of “gradient descent” in training neural networks?

To increase the loss function
To minimize the loss function
To normalize input data
To calculate feature importance

Explanation: Gradient Descent minimizes the loss by updating model parameters opposite to the gradient direction.

Reference: Gradient Descent Explained - Coursera

Q11. In which situation would you most likely use clustering algorithms?

When you have labeled data
When you want to group similar data points without labels
When you want to perform regression analysis
When predicting future prices

Explanation: Clustering is an unsupervised learning method used to group unlabeled data by similarity.

Q12. What does a confusion matrix show?

Model loss over time
True positives, false positives, true negatives, and false negatives
Feature correlations
Decision boundaries

Explanation: A confusion matrix summarizes prediction outcomes for classification models.

Q13. What is the purpose of feature scaling?

To ensure all features contribute equally to model training
To remove irrelevant features
To encode categorical variables
To reduce overfitting

Explanation: Feature scaling standardizes numerical ranges, helping algorithms like gradient descent converge efficiently.

Q14. What does “bias-variance tradeoff” refer to?

The balance between model simplicity and its ability to generalize
The difference between input and output bias
The trade between accuracy and recall
The time-memory tradeoff

Explanation: Low bias can cause overfitting, while high bias leads to underfitting — good models find balance.

Q15. What metric is best for imbalanced classification problems?

Accuracy
MSE
F1-Score
R² Score

Explanation: F1-Score balances precision and recall, making it ideal for imbalanced datasets.

Q16. What is the main purpose of a validation set?

To train the model
To tune hyperparameters and evaluate performance
To test model performance on unseen data
To normalize the input features

Explanation: Validation sets help in model selection and hyperparameter tuning before final testing.

Q17. Which algorithm is best suited for dimensionality reduction?

KNN
Naive Bayes
PCA (Principal Component Analysis)
Random Forest

Explanation: PCA transforms high-dimensional data into fewer dimensions while retaining variance.

Q18. Which type of neural network is best for sequence data?

Explanation: RNNs (Recurrent Neural Networks) are designed for sequential inputs like text or time series.

Q19. What is the purpose of regularization in machine learning?

To prevent overfitting by adding penalty terms to the loss function
To normalize inputs
To improve data quality
To increase model complexity

Explanation: Regularization methods like L1/L2 reduce overfitting by constraining model weights.

Q20. Which of the following is NOT a type of ensemble method?

Random Forest
Gradient Boosting
Bagging
Dropout

Explanation: Dropout is a regularization technique in neural networks, not an ensemble method.

Q21. What is the main advantage of using cross-validation?

Provides a more reliable estimate of model performance
Reduces the need for a validation set
Speeds up training
Simplifies the model

Explanation: Cross-validation uses multiple folds to reduce variance in performance estimates.

Q22. Which statement best describes unsupervised learning?

Algorithms find patterns in data without predefined labels
Algorithms require labeled input
Models are trained with rewards and penalties
It’s only used for classification problems

Explanation: Unsupervised learning discovers hidden structures in unlabeled data.

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