- Supervised Learning
- Classification
- Regression
- Time Series Forecasting
- Unsupervised Learning
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Semi-Supervised Learning
- Reinforcement Learning(ML)
- Deep Learning(ML)
- Transfer Learning(ML)
- Ensemble Learning
- Explainable AI (XAI)
- Bayesian Learning
- Decision Trees
- Support Vector Machines (SVMs)
- Instance-Based Learning
- Rule-Based Learning
- Neural Networks
- Evolutionary Algorithms
- Meta-Learning
- Multi-Task Learning
- Metric Learning
- Few-Shot Learning
- Adversarial Learning
- Data Pre Processing
- Natural Language Processing(ML)
Semi-Supervised Learning QUIZ QUESTIONS
Question: 1
What is the primary goal of semi-supervised learning?
Question: 2
In the context of semi-supervised learning, what is the "smoothness assumption"?
Question: 3
Which of the following methods is commonly used in semi-supervised learning to propagate labels from labeled to unlabeled data points?
Question: 4
How does the self-training approach in semi-supervised learning work?
Question: 5
What is the main difference between transductive and inductive semi-supervised learning?
Question: 6
Which of the following is NOT a typical challenge faced in semi-supervised learning?
Question: 7
In the context of semi-supervised learning, what is "co-training"?
Question: 8
Which of the following is a primary advantage of using generative models in semi-supervised learning?
Question: 9
In the context of semi-supervised learning, what is "pseudo-labeling"?
Question: 10
Which of the following is NOT a common approach used in semi-supervised learning?
Question: 11
How does the "cluster assumption" relate to semi-supervised learning?
Question: 12
What is the main advantage of using semi-supervised learning over supervised learning?
Question: 13
Which of the following is a common strategy to combine labeled and unlabeled data in semi-supervised learning?
Question: 14
What is the main challenge in using self-training for semi-supervised learning?
Question: 15
In the context of semi-supervised learning, what is the purpose of "consistency regularization"?
Question: 16
In semi-supervised learning, which of the following is an advantage of using graph-based methods?
Question: 17
Which of the following is a disadvantage of using semi-supervised learning?
Question: 18
What is the main assumption underlying most semi-supervised learning algorithms?
Question: 19
Which of the following is NOT a common assumption in semi-supervised learning?
Question: 20
In the context of semi-supervised learning, what is the "manifold assumption"?
Question: 21
Which of the following techniques is typically NOT used for semi-supervised learning?
Question: 22
In which scenario is semi-supervised learning most useful?
Question: 23
What is the main advantage of using transductive semi-supervised learning over inductive semi-supervised learning?
Question: 24
How do generative models differ from discriminative models in the context of semi-supervised learning?
Question: 25
Which of the following is a common approach to incorporating unlabeled data into semi-supervised learning algorithms based on deep learning?
Question: 26
In the context of semi-supervised learning, which of the following is an advantage of using discriminative models over generative models?
Question: 27
In the context of semi-supervised learning, what is the "low-density separation" assumption?
Question: 28
What is a potential drawback of using graph-based methods in semi-supervised learning?
Question: 29
Which of the following techniques can be used to address the risk of propagating incorrect labels in semi-supervised learning?
Question: 30
In semi-supervised learning, why is it important to have a diverse set of labeled instances?