Which method can be seen as a probabilistic extension to k-means clustering, allowing soft assignments of data points?
- Mean-Shift Clustering
- Hierarchical Clustering
- Expectation-Maximization (EM)
- DBSCAN Clustering
The Expectation-Maximization (EM) method is a probabilistic extension to k-means, allowing soft assignments of data points based on probability distributions.
Q-learning is an off-policy algorithm because it learns the value of the optimal policy's actions, which may be different from the current ________'s actions.
- Agent's
- Environment's
- Agent's or Environment's
- Policy's
Q-learning is indeed an off-policy algorithm, as it learns the value of the optimal policy's actions (maximizing expected rewards) irrespective of the current environment's actions.
In the Actor-Critic approach, the ________ provides a gradient for policy improvement based on feedback.
- Critic
- Agent
- Selector
- Actor
In the Actor-Critic approach, the Critic evaluates the policy and provides a gradient that guides policy improvement based on feedback, making it a fundamental element of the approach.
ICA is often used to separate ________ that have been mixed into a single data source.
- Signals
- Components
- Patterns
- Features
Independent Component Analysis (ICA) is used to separate mixed components in a data source, making 'Components' the correct answer.
A medical imaging company is trying to diagnose diseases from X-ray images. Considering the spatial structure and patterns in these images, which type of neural network would be most appropriate?
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Feedforward Neural Network
- Radial Basis Function Network
A Convolutional Neural Network (CNN) is designed to capture spatial patterns and structures in images effectively, making it suitable for image analysis, such as X-ray diagnosis.
With the aid of machine learning, wearable devices can predict potential health events by analyzing ________ data.
- Sensor
- Biometric
- Personal
- Lifestyle
Machine learning applied to wearable devices can predict potential health events by analyzing biometric data. This includes information such as heart rate, blood pressure, and other physiological indicators that provide insights into the wearer's health status.
Which method involves reducing the number of input variables when developing a predictive model?
- Dimensionality Reduction
- Feature Expansion
- Feature Scaling
- Model Training
Dimensionality reduction is the process of reducing the number of input variables by selecting the most informative ones, combining them, or transforming them into a lower-dimensional space. This helps simplify models and can improve their efficiency and performance.
A bank wants to use transaction details to determine the likelihood that a transaction is fraudulent. The outcome is either "fraudulent" or "not fraudulent." Which regression method would be ideal for this purpose?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Logistic Regression is the ideal choice for binary classification tasks, like fraud detection (fraudulent or not fraudulent). It models the probability of an event occurring, making it the right tool for this scenario.
How does the Actor-Critic model differ from traditional Q-learning in reinforcement learning?
- In Actor-Critic, the Actor and Critic are separate entities.
- Q-learning uses value iteration, while Actor-Critic uses policy iteration.
- Actor-Critic relies on neural networks, while Q-learning uses decision trees.
- In Q-learning, the Critic updates the policy.
The Actor-Critic model is different from traditional Q-learning as it separates the task of policy learning (Actor) from value estimation (Critic), whereas in Q-learning, these functions are often combined. This separation allows for more flexibility and efficiency in learning policies in complex environments.
The ________ in the Actor-Critic model estimates the value function of the current policy.
- Critic
- Actor
- Agent
- Environment
In the Actor-Critic model, the "Critic" estimates the value function of the current policy. It assesses how good the chosen actions are, guiding the "Actor" in improving its policy based on these value estimates.
What is the primary advantage of using LSTMs and GRUs over basic RNNs?
- Handling Vanishing Gradient
- Simplicity and Speed
- Memory Efficiency
- Higher Prediction Accuracy
LSTMs and GRUs offer an advantage in handling the vanishing gradient problem, which is a significant limitation of basic RNNs. Their gated mechanisms help mitigate this issue, allowing for better learning of long-term dependencies and improved performance in tasks involving sequential data.
What is the primary purpose of regularization in machine learning?
- Enhance model complexity
- Improve model accuracy
- Prevent overfitting
- Promote underfitting
Regularization techniques aim to prevent overfitting by adding a penalty term to the model's loss function. This encourages the model to be less complex, reducing the risk of overfitting while maintaining good performance.