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.
For a medical test, it's crucial to minimize the number of false negatives. Which metric would be particularly important to optimize in this context?
- Accuracy
- F1 Score
- Precision
- Recall
In the context of a medical test, minimizing false negatives is vital because you want to avoid missing actual positive cases. This emphasizes the importance of optimizing recall, which measures the ability of the test to correctly identify all positive cases, even at the expense of more false positives.
When visualizing high-dimensional data in two or three dimensions, one might use PCA to project the data onto the first few ________.
- Principal Components
- Data Points
- Dimensions
- Eigenvalues
PCA (Principal Component Analysis) is used to reduce the dimensionality of data by projecting it onto the first few Principal Components, which are linear combinations of the original dimensions.
Imagine a scenario where an online learning platform wants to categorize its vast number of courses into different topics. The platform doesn't have predefined categories but wants the algorithm to determine them based on course content. This task would best be accomplished using which learning approach?
- Clustering
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
Unsupervised learning is the most suitable approach. Here, the algorithm should discover inherent structures or clusters within the courses without any predefined categories, making unsupervised learning a fitting choice.
Which term refers to using a model that has already been trained on a large dataset and fine-tuning it for a specific task?
- Model adaptation
- Model transformation
- Model modification
- Fine-tuning
Fine-tuning is the process of taking a pre-trained model and adjusting it to perform a specific task. It's a crucial step in transfer learning, where the model adapts its features and parameters to suit the new task.
One of the applications of NLP in healthcare is to assist in ________, which involves the conversion of voice-recorded notes into text format.
- Transcription
- Speech Recognition
- Note Parsing
- Audio Conversion
NLP (Natural Language Processing) can be employed for transcription tasks in healthcare, where voice-recorded notes from medical professionals are converted into text format. This conversion makes the notes more accessible and searchable for medical records.
Which tool or technique is often used to make complex machine learning models more understandable for humans?
- Explainable AI (XAI)
- Reinforcement Learning
- Principal Component Analysis
- Gradient Boosting
Explainable AI (XAI) techniques are employed to simplify complex machine learning models, making them interpretable, and providing insights into model decisions.
Techniques like backward elimination, forward selection, and recursive feature elimination are used for ________ in machine learning.
- Cross-Validation
- Data Preprocessing
- Feature Selection
- Model Training
Techniques like backward elimination, forward selection, and recursive feature elimination are used for feature selection in machine learning. Feature selection helps identify the most relevant features for building accurate models and can improve model efficiency.