COELE1-2

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

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

2. "Transfer Learning" in deep learning involves:

A) Training a model from scratch on every new problem.
B) 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.
C) Using only unsupervised learning techniques.
D) Forgetting everything a model has learned.

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

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

4. The architecture of a typical autoencoder consists of:

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

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

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

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

8. The "F1 Score" is:

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

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

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

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

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

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) 0.0.
D) 0.5.

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

A) Increase the size of the training dataset.
B) Obtain a more robust estimate of model performance by training and evaluating the model K times on different splits of the data.
C) Visualize high-dimensional data.
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) A single, pre-defined rule.
B) A random selection from the training set.
C) The majority vote among its K closest neighbors in the feature space.
D) The output of a linear function.u

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

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

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

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

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

A) Is randomly oriented.
B) Is perpendicular to all other components.
C) Captures the least possible variance in the data.
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 projected onto a single dimension.
B) The data is perfectly classified into known labels.
C) The within-cluster variance is minimized.
D) The between-cluster variance is minimized.

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

A) Initialize the cluster centroids.
B) Evaluate the accuracy of a classification model.
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) Do not use probability in their predictions.
D) Always have the lowest possible accuracy.

20. "Logistic Regression" is fundamentally a:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

29. "Regularization" is a technique used to:

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

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

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

31. "Hyperparameters" are:

A) The output predictions of the model.
B) The parameters that the model learns during training (e.g., weights in a neural network).
C) The input features 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) Deploying the final model.
B) Searching for the best combination of hyperparameters that results in the best model performance.
C) Training the model's internal weights.
D) Cleaning the raw data.

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

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

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

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

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

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

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

A) Regression problems.
B) Multi-class classification problems, as it outputs a probability distribution over the possible classes.
C) Unsupervised learning 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 only used for unsupervised learning.
C) Is guaranteed to find the global minimum for any function.
D) Does not require any hyperparameters.

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

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

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

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

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

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

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

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

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

A) It is only used for clustering unlabeled data.
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 a simpler and less powerful approach.

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

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

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

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

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

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

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

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

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

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

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

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

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