While supervised learning requires explicit labels, ________ learning operates on data without explicit instructions.
- Deep
- Reinforcement
- Semi-supervised
- Unsupervised
In machine learning, unsupervised learning operates on data without explicit labels or instructions. Supervised learning, on the other hand, relies heavily on labeled data.
CNNs are particularly effective for image data due to their ability to preserve the ________ structure of the data.
- Spatial
- Color
- Temporal
- Frequency
CNNs are effective for image data due to their ability to preserve the spatial structure of the data, which is crucial for detecting patterns in pixels' proximity.
While linear regression is concerned with estimating the mean of the dependent variable, logistic regression estimates the probability that the dependent variable belongs to a particular ________.
- Category
- Class
- Cluster
- Group
Logistic regression estimates the probability that the dependent variable belongs to a particular class or category. Unlike linear regression, which predicts continuous values, logistic regression is used for classification problems.
Which type of learning is typically employed when there's neither complete supervision nor complete absence of supervision, but a mix where an agent learns to act in an environment?
- Reinforcement Learning
- Self-supervised Learning
- Semi-supervised Learning
- Unsupervised Learning
Semi-supervised Learning fits this scenario. It combines labeled and unlabeled data to train a model. In situations where you have some labeled data but not enough for full supervision, or when labeling is expensive, semi-supervised learning is a practical choice.
Why is Independent Component Analysis (ICA) primarily used in applications like audio signal processing?
- It's more computationally efficient
- It separates mixed sources effectively
- It requires less data for training
- It's based on supervised learning
ICA is used in audio signal processing because it can effectively separate mixed sources, making it useful for source separation and blind signal separation tasks.
A healthcare company wants to classify patients into risk categories based on their medical history. They have a vast amount of patient data, but the relationships between variables are complex and non-linear. Which algorithm might be more suitable for this task?
- Decision Trees
- K-Nearest Neighbors (K-NN)
- Logistic Regression
- Naive Bayes
Decision Trees are suitable for complex and non-linear relationships between variables. They can capture intricate patterns in patient data, making them effective for risk categorization in healthcare.
In pharmacology, machine learning can aid in the process of drug discovery by predicting potential ________ of new compounds.
- Toxicity
- Flavor Profile
- Market Demand
- Molecular Structure
Machine learning can predict potential toxicity of new compounds by analyzing their chemical properties and interactions in pharmacology.
The Naive Bayes classifier assumes that the presence or absence of a particular feature of a class is ________ of the presence or absence of any other feature.
- Correlated
- Dependent
- Independent
- Unrelated
Naive Bayes assumes that features are independent of each other. This simplifying assumption helps make the algorithm computationally tractable but might not hold in all real-world cases.
Regularization techniques help in preventing overfitting. Which of these is NOT a regularization technique: Batch Normalization, Dropout, Adam Optimizer, L1 Regularization?
- Adam Optimizer
- Batch Normalization
- Dropout
- L1 Regularization
Adam Optimizer is not a regularization technique. It's an optimization algorithm used in training neural networks, while the others are regularization methods.
A medical research team is studying the relationship between various health metrics (like blood pressure, cholesterol level) and the likelihood of developing a certain disease. The outcome is binary (disease: yes/no). Which regression model should they employ?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Logistic Regression is the appropriate choice for binary outcomes, such as the likelihood of developing a disease (yes/no). It models the probability of a binary outcome based on predictor variables, making it well-suited for this medical research.