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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

A) 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).
B) The output predictions of the model.
C) The input features of the model.
D) The parameters that the model learns during training (e.g., weights in a neural network).

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

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

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

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

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

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

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

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

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

A) The learning rate is set too high.
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 features are not scaled properly.

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

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

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

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

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

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

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

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