Q-learning is a type of ________ learning algorithm that aims to find the best action to take given a current state.
- Reinforcement
- Supervised
- Unsupervised
- Semi-supervised
Q-learning is a type of reinforcement learning that focuses on finding the best action to take in a given state to maximize cumulative rewards.
In a scenario with noisy data, increasing the value of 'k' in the k-NN algorithm can help to ________ the noise.
- Amplify
- Eliminate
- Introduce
- Preserve
Increasing the value of 'k' in the k-NN algorithm can help eliminate noise. A higher 'k' value smooths the decision boundaries, reducing the impact of individual noisy data points.
In K-means clustering, the algorithm iteratively updates the cluster centers until the within-cluster sum of squares is ________.
- Minimized
- Equal to 0
- Maximized
- Converged
In K-means clustering, the algorithm aims to minimize the within-cluster sum of squares (WCSS). This represents the total variance within clusters. As the algorithm iteratively updates the cluster centers, the goal is to minimize the WCSS, making "Minimized" the correct option.
In the context of RNNs, what problem does the introduction of gating mechanisms in LSTMs and GRUs aim to address?
- Vanishing and Exploding Gradients
- Overfitting and Data Loss
- Dimensionality Reduction and Compression
- Sequence Length Reduction and Truncation
The introduction of gating mechanisms in LSTMs and GRUs aims to address the problem of vanishing and exploding gradients, which occur during training due to the backpropagation of errors over long sequences. These mechanisms help RNNs capture long-range dependencies in data.
The multi-armed bandit problem is a classic problem in which domain?
- Sequential Decision-Making Problems
- Natural Language Processing
- Computer Graphics
- Speech Recognition
The multi-armed bandit problem falls under the domain of Sequential Decision-Making Problems, specifically addressing scenarios where a decision must be made over time with limited resources.
The ability of SVMs to handle non-linear decision boundaries is achieved by transforming the input data into a higher-dimensional space using a ______.
- Classifier
- Dimensionality Reduction
- Ensemble
- Kernel
SVMs use a mathematical function called a kernel to transform data into a higher-dimensional space, enabling them to handle non-linear decision boundaries effectively.
What is the potential consequence of deploying a non-interpretable machine learning model in a critical sector, such as medical diagnosis?
- Inability to explain decisions
- Improved accuracy
- Faster decision-making
- Better generalization
Deploying a non-interpretable model can result in a lack of transparency, making it challenging to understand how and why the model makes specific medical diagnosis decisions. This lack of transparency can be risky in critical sectors.
Which of the following techniques is used to estimate future rewards in reinforcement learning?
- Q-Learning
- Gradient Descent
- Principal Component Analysis
- K-Means Clustering
Q-Learning is a technique in reinforcement learning used to estimate future rewards associated with taking actions in different states.
What is the main challenge faced by NLP systems when processing clinical notes in electronic health records?
- Variability in clinical language
- Availability of data
- Lack of computational resources
- Precision and recall
Clinical notes often use varied and context-specific language, making it challenging for NLP systems to accurately interpret and extract information from electronic health records. This variability can impact system accuracy.
What type of neural network is designed for encoding input data into a compressed representation and then decoding it back to its original form?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Autoencoder
- Long Short-Term Memory (LSTM)
Autoencoders are neural networks designed for this task. They consist of an encoder network that compresses input data into a compact representation and a decoder network that reconstructs the original data from this representation.
In the context of GANs, the generator tries to produce fake data, while the discriminator tries to ________ between real and fake data.
- Differentiate
- Discriminate
- Generate
- Classify
The discriminator in a GAN is responsible for distinguishing or discriminating between real and fake data, not generating or differentiating.
In hierarchical clustering, the ________ method involves merging the closest clusters in each iteration.
- Agglomerative
- Divisive
- DBSCAN
- OPTICS
In hierarchical clustering, the Agglomerative method starts with individual data points as clusters and iteratively merges the closest clusters, creating a hierarchy. "Agglomerative" is the correct option, representing the bottom-up approach of this clustering method.