Ensuring that a machine learning model does not unintentionally favor or discriminate against certain groups is ensuring its ________.
- Fairness
- Accuracy
- Efficiency
- Robustness
Ensuring fairness in machine learning models means preventing biases and discrimination in model predictions across different groups.
A common activation function used in CNNs that helps introduce non-linearity is ________.
- Sigmoid
- ReLU
- Linear
- Tanh
The ReLU (Rectified Linear Unit) activation function is widely used in CNNs for its ability to introduce non-linearity into the model, crucial for learning complex patterns.
In the multi-armed bandit problem, the challenge is to balance between exploration of arms and ________ of the best-known arm.
- Exploitation
- Reward accumulation
- Arm selection
- Probability estimation
The multi-armed bandit problem involves the trade-off between exploration (trying new arms) and exploitation (selecting the best-known arm).
RNNs are particularly suitable for tasks like ________ because of their ability to handle sequences.
- Sentiment Analysis
- Image Classification
- Sequence Prediction
- Audio Recognition
RNNs excel in tasks that involve sequences, such as sequence prediction, where the previous elements influence the future ones.
The output of a GAN, after training, is a/an ________ that closely resembles the real data.
- Image
- Noise
- Anomaly
- Vector
The output of a GAN is typically an image, which is generated to closely resemble the real data it was trained on.
A bank uses a machine learning model for loan approvals. However, it's observed that individuals from certain ethnic backgrounds are consistently getting rejected more than others, despite having similar financial profiles. This raises concerns related to which aspect of machine learning?
- Fairness & Bias
- Accuracy
- Data Preprocessing
- Model Training
This situation pertains to fairness and bias in machine learning, specifically, unfair outcomes or discrimination against certain ethnic groups, which highlights ethical concerns. It's important to address these biases for equitable decision-making.
How does NLP handle the nuances and variations in medical terminologies across different healthcare systems?
- Named Entity Recognition and Mapping
- Machine Translation of Medical Terms
- Simple Text Matching
- Manual Reconciliation
NLP utilizes techniques like Named Entity Recognition to identify and map medical terms to a standardized vocabulary, addressing the nuances and variations in medical terminologies across different healthcare systems. This enables consistent analysis and retrieval of information.
What is the primary objective of Generative Adversarial Networks (GANs)?
- Data Classification
- Data Generation
- Data Storage
- Data Analysis
The primary objective of GANs is data generation. GANs consist of a generator that creates data samples to closely resemble real data, aiding in tasks like image generation.
A medical diagnosis AI system provides a diagnosis but does not give any rationale or reasoning behind it. What aspect of machine learning is this system lacking?
- Interpretability
- Classification
- Model Complexity
- Feature Engineering
The system's lack of providing rationale or reasoning is a deficiency in interpretability. In medical AI, it's crucial for doctors to understand why a diagnosis was made to trust and make informed decisions based on the AI's recommendations.
When training a robot to play a game where it gets points for good moves and loses points for bad ones, which learning approach would be most appropriate?
- Reinforcement learning
- Semi-supervised learning
- Supervised learning
- Unsupervised learning
Reinforcement learning is the most appropriate approach for training a robot to play a game where it receives rewards for good moves and penalties for bad ones. In reinforcement learning, the agent learns through trial and error, optimizing its actions to maximize cumulative rewards. Supervised learning would require explicit labels for each move, which are typically not available in this context. Unsupervised and semi-supervised learning are not suitable for tasks with rewards and penalties.