COELE1-2 - Quiz

1. "Mini-batch Gradient Descent" is often preferred because it:

A) Is only applicable to linear models.
B) Offers a balance between the efficiency of batch GD and the robustness of SGD.
C) Is guaranteed to converge faster than any other method.
D) Does not require a loss function.

2. "Transfer Learning" in deep learning involves:

A) Taking a model pre-trained on a large dataset (e.g., ImageNet) and fine-tuning it for a new, specific task with a smaller dataset.
B) Forgetting everything a model has learned.
C) Using only unsupervised learning techniques.
D) Training a model from scratch on every new problem.

3. An "Autoencoder" is a type of neural network primarily used for:

A) Predicting continuous values in a regression task.
B) Unsupervised learning tasks like dimensionality reduction and data denoising.b
C) Reinforcement learning.
D) Supervised classification of images.

4. The architecture of a typical autoencoder consists of:

A) Only a single layer of perceptrons.
B) A single output neuron with a linear activation.
C) An encoder that compresses the input and a decoder that reconstructs the input from the compression.
D) A convolutional layer followed by an RNN layer.

5. In the context of model evaluation for classification, "Accuracy" is defined as:

A) The proportion of positive identifications that were actually correct.
B) The proportion of actual positives that were identified correctly.
C) The harmonic mean of precision and recall.
D) The proportion of total predictions that were correct.

6. "Precision" is an important metric when:

A) The cost of false positives is high (e.g., in spam detection, where you don't want to flag legitimate emails as spam).
B) You need a single metric that combines precision and recall.
C) You are evaluating a regression model.
D) The cost of false negatives is high (e.g., in disease screening, where you don't want to miss a sick patient).

7. "Recall" is an important metric when:

A) You are evaluating a clustering model.
B) The cost of false positives is high (e.g., in spam detection).
C) The cost of false negatives is high (e.g., in disease screening, where you don't want to miss a sick patient).
D) You need a single metric that combines precision and recall.

8. The "F1 Score" is:

A) The harmonic mean of precision and recall, providing a single score that balances both concerns.
B) A metric used exclusively for regression.
C) The difference between precision and recall.
D) The arithmetic mean of precision and recall.

9. For a regression model, the "Mean Squared Error" (MSE) measures:

A) The average of the squares of the errors between predicted and actual values.
B) The accuracy of a classification model.
C) The variance of the input features.
D) The total number of misclassified instances.

10. The "ROC Curve" is a tool used to evaluate:

A) The performance of a binary classification model at various classification thresholds.
B) The loss of a regression model over time.
C) The architecture of a neural network.
D) The clustering quality of a K-means algorithm.

11. "Area Under the ROC Curve" (AUC) provides an aggregate measure of performance across all possible classification thresholds. A perfect model has an AUC of:

A) 1.0.
B) -1.0.
C) 0.5.
D) 0.0.

12. "K-fold Cross-Validation" is a technique used to:

A) Obtain a more robust estimate of model performance by training and evaluating the model K times on different splits of the data.
B) Visualize high-dimensional data.
C) Increase the size of the training dataset.
D) Replace the need for a separate test set.

13. In the K-Nearest Neighbors (K-NN) algorithm for classification, the class of a new data point is determined by:

A) The output of a linear function.u
B) The majority vote among its K closest neighbors in the feature space.
C) A random selection from the training set.
D) A single, pre-defined rule.

14. The parameter 'K' in the K-NN algorithm:

A) Is always set to 1 for the best performance.
B) Controls the model's flexibility. A small K can lead to overfitting, while a large K can lead to underfitting.
C) Is the learning rate for the algorithm.
D) Is the number of features in the dataset.

15. "Principal Component Analysis" (PCA) works by:

A) Clustering data into K groups.
B) Finding new, uncorrelated dimensions (principal components) that capture the maximum variance in the data.u
C) Predicting a target variable using linear combinations of features.
D) Classifying data using a decision boundary.

16. The first principal component in PCA is the direction in the feature space that:

A) Is perpendicular to all other components.
B) Captures the least possible variance in the data.
C) Is randomly oriented.
D) Captures the greatest possible variance in the data.

17. "K-Means Clustering" aims to partition data into K clusters such that:

A) The data is perfectly classified into known labels.
B) The data is projected onto a single dimension.
C) The between-cluster variance is minimized.
D) The within-cluster variance is minimized.

18. The "Elbow Method" is a heuristic used in K-Means to:

A) Evaluate the accuracy of a classification model.
B) Initialize the cluster centroids.
C) Determine the learning rate for gradient descent.
D) Help choose the optimal number of clusters K by looking for a "bend" in the plot of within-cluster variance.

19. "Naive Bayes" classifiers are called "naive" because they:

A) Make a strong (naive) assumption that all features are conditionally independent given the class label.
B) Are very simple and cannot handle complex data.
C) Always have the lowest possible accuracy.
D) Do not use probability in their predictions.

20. "Logistic Regression" is fundamentally a:

A) Dimensionality reduction technique.
B) Regression algorithm for predicting continuous values.
C) Classification algorithm that models the probability of a binary outcome using a logistic function.
D) Clustering algorithm for grouping unlabeled data.

21. The output of a logistic regression model is a value between 0 and 1, which represents the:

A) Probability that the input belongs to a particular class.
B) Number of features in the input.
C) Distance to the decision boundary.
D) Exact value of the target variable.

22. A "Random Forest" is an ensemble method that combines multiple:

A) K-NN models.
B) Decision Trees to reduce overfitting and improve generalization.
C) Linear Regression models.
D) Support Vector Machines.

23. The "bagging" technique in a Random Forest helps to:

A) Increase the speed of a single decision tree.
B) Reduce bias by making trees more complex.
C) Perform feature extraction like PCA.
D) Reduce variance by training individual trees on random subsets of the data and averaging their results

24. "Gradient Boosting" machines (e.g., XGBoost) are ensemble methods that:

A) Build models sequentially, where each new model corrects the errors of the previous ones.
B) Build all models independently and average them.
C) Do not require any parameter tuning.
D) Are exclusively used for unsupervised learning.

25. The term "feature engineering" refers to:

A) The process of using domain knowledge to create new input features that make machine learning algorithms work better.
B) The evaluation of a model's final performance.
C) The automatic learning of features by a deep neural network.
D) The process of deleting all features from a dataset.

26. "One-hot encoding" is a preprocessing technique used to:

A) Normalize continuous numerical features.
B) Convert categorical variables into a binary (0/1) format that can be provided to ML algorithms.
C) Cluster similar data points together.
D) Reduce the dimensionality of image data.

27. "Feature scaling" (e.g., normalization or standardization) is often crucial for algorithms that:

A) Are used for association rule learning.
B) Are used for clustering only.
C) Are based on distance calculations or gradient descent, such as SVM and Neural Networks.
D) Are based on tree-based models like Decision Trees and Random Forests.

28. The "curse of dimensionality" refers to the problem that:

A) Dimensionality reduction always improves model performance.
B) All datasets should have as many features as possible.
C) There are never enough features to train a good model.
D) As the number of features grows, the data becomes increasingly sparse, making it harder to find meaningful patterns.

29. "Regularization" is a technique used to:

A) Prevent overfitting by adding a penalty term to the loss function that discourages complex models.
B) Increase the variance of a model.
C) Make models more complex to fit the training data better.
D) Speed up the training time of a model.

30. L1 Regularization (Lasso) can often lead to:

A) A decrease in model interpretability.
B) All features having non-zero weights.
C) Sparse models where the weights of less important features are driven to zero, effectively performing feature selection.
D) Increased model complexity.

31. "Hyperparameters" are:

A) The input features of the model.
B) The parameters that the model learns during training (e.g., weights in a neural network).
C) The output predictions of the model.
D) Configuration settings for the learning algorithm that are not learned from the data and must be set prior to training (e.g., learning rate, K in K-NN).

32. The process of "Hyperparameter Tuning" involves:

A) Searching for the best combination of hyperparameters that results in the best model performance.
B) Deploying the final model.
C) Cleaning the raw data.
D) Training the model's internal weights.

33. "Grid Search" is a common method for hyperparameter tuning that involves:

A) Using a separate neural network to predict the best hyperparameters.
B) Randomly sampling hyperparameter combinations from a distribution.
C) Ignoring hyperparameters altogether.
D) Exhaustively searching over a specified set of hyperparameter values.

34. "Early Stopping" is a form of regularization that works by:

A) Starting the training process later than scheduled.
B) Using a very small learning rate.
C) Halting the training process when performance on a validation set starts to degrade, indicating the onset of overfitting.
D) Stopping the training after a fixed, very short number of epochs.

35. A "Vanilla" neural network, also known as a Multilayer Perceptron (MLP), is typically composed of:

A) Convolutional layers for processing images.
B) Recurrent layers for processing sequences.
C) A single layer of neurons.
D) Fully connected layers, where each neuron in one layer is connected to every neuron in the next layer.

36. The "softmax" activation function is commonly used in the output layer of a neural network for:

A) Multi-class classification problems, as it outputs a probability distribution over the possible classes.
B) Unsupervised learning problems.
C) Regression problems.
D) Binary classification problems.

37. The "Adam" optimizer is an adaptive learning rate algorithm that is often preferred because it:

A) Combines the advantages of two other extensions of stochastic gradient descent, AdaGrad and RMSProp.
B) Is guaranteed to find the global minimum for any function.
C) Is only used for unsupervised learning.
D) Does not require any hyperparameters.

38. "Batch Normalization" is a technique used to:

A) Replace the need for an activation function.
B) Normalize the entire dataset before feeding it into the network.
C) Increase the batch size during training.
D) Improve the stability and speed of neural network training by normalizing the inputs to each layer.

39. The "confusion matrix" is a table that is used to describe the performance of a:

A) Dimensionality reduction technique's effectiveness.
B) Clustering algorithm's group assignments.
C) Regression model's accuracy.
D) Classification model on a set of test data for which the true values are known.

40. In a confusion matrix, the "true positives" are the cases where:

A) The model correctly predicted the negative class.
B) The model incorrectly predicted the positive class.
C) The model correctly predicted the positive class.
D) The model incorrectly predicted the negative class.

41. The problem of "imbalanced classes" occurs when:

A) The learning rate is set too high.
B) One class in the training data has significantly more examples than another, which can bias the model.
C) The model is too complex for the data.
D) The features are not scaled properly.

42. A technique to address imbalanced classes is "SMOTE," which:

A) Generates synthetic examples for the minority class to balance the dataset.
B) Ignores the minority class completely.
C) Deletes examples from the majority class at random.
D) Combines all classes into one.

43. "Reinforcement Learning" differs from supervised and unsupervised learning in that:

A) It is a simpler and less powerful approach.
B) It requires a fully labeled dataset for training.
C) It learns by interacting with an environment and receiving rewards or penalties for actions, without a labeled dataset.
D) It is only used for clustering unlabeled data.

44. "Q-Learning" is a popular algorithm in reinforcement learning that learns:

A) The principal components of a state space.
B) A clustering of possible actions.
C) A policy that tells an agent what action to take under what circumstances by learning a value function.
D) A decision tree for classification.

45. "Natural Language Processing" (NLP) often uses supervised learning for tasks like:

A) Generating new, original text without any input.
B) Reducing the dimensionality of word vectors.
C) Grouping similar news articles without labels.
D) Sentiment analysis, where text is classified as positive, negative, or neutral.

46. "Word Embeddings" (like Word2Vec) are techniques that:

A) Represent words as simple one-hot encoded vectors.
B) Are a type of clustering algorithm.
C) Represent words as dense vectors in a continuous space, capturing semantic meaning.
D) Are used only for image classification.

47. A "Generative Adversarial Network" (GAN) consists of two networks:

A) A single, large Regression network.
B) A Generator and a Discriminator, which are trained in opposition to each other.
C) An Encoder and a Decoder for compression.
D) Two identical Convolutional Neural Networks.

48. The "Generator" in a GAN is responsible for:

A) Reducing the dimensionality of the input.
B) Classifying input images into categories.
C) Discriminating between real and fake data.
D) Creating new, synthetic data that is indistinguishable from real data.

49. The "Discriminator" in a GAN is essentially a:

A) Regression model predicting a continuous value.
B) Clustering algorithm grouping similar images.
C) Binary classifier that tries to correctly label data as real (from the dataset) or fake (from the generator).
D) Dimensionality reduction technique.

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