Types of Learning Flashcards
This deck highlights the different types of machine learning.
What is supervised learning?
Supervised learning is a type of machine learning where the machine is trained on a set of labeled data. The labels tell the machine what the output should be for a given input.
What are some examples of supervised learning algorithms?
Some examples of supervised learning algorithms include linear regression, logistic regression, and decision trees.
What are the advantages of supervised learning?
The advantages of supervised learning include that it is a relatively simple and straightforward approach, and it can be very effective for tasks where there is a lot of labeled data available.
What are the disadvantages of supervised learning?
The disadvantages of supervised learning include that it can be difficult to collect labeled data, and it can be computationally expensive to train the machine on a large dataset.
What is unsupervised learning?
Unsupervised learning is a type of machine learning where the machine is not given any labels. The machine has to learn to identify patterns in the data on its own.
What are some examples of unsupervised learning algorithms?
Some examples of unsupervised learning algorithms include k-means clustering, principal component analysis, and association rule mining.
What are the advantages of unsupervised learning?
The advantages of unsupervised learning include that it can be used to find patterns in data that would be difficult or impossible to find with supervised learning.
What are the disadvantages of unsupervised learning?
The disadvantages of unsupervised learning include that it can be difficult to interpret the results, and it can be less effective than supervised learning for tasks where there is a clear target output.
What is semi-supervised learning?
Semi-supervised learning is a type of machine learning that combines supervised learning and unsupervised learning. The machine is trained on a set of labeled data and a set of unlabeled data.
What are some examples of semi-supervised learning algorithms?
Some examples of semi-supervised learning algorithms include transductive SVMs and label propagation.
What are the advantages of semi-supervised learning?
The advantages of semi-supervised learning include that it can be more effective than supervised learning when there is a limited amount of labeled data available.
What are the disadvantages of semi-supervised learning?
The disadvantages of semi-supervised learning include that it can be more difficult to train than supervised learning, and it can be less effective than unsupervised learning when there is a lot of unlabeled data available.
What is reinforcement learning?
Reinforcement learning is a type of machine learning where the machine learns by trial and error. The machine is given a reward for taking actions that lead to desired outcomes, and it is penalized for taking actions that lead to undesired outcomes.
What are some examples of reinforcement learning algorithms?
Some examples of reinforcement learning algorithms include Q-learning and SARSA.
What are the advantages of reinforcement learning?
The advantages of reinforcement learning include that it can be used to solve problems where there is no clear target output, and it can be very effective for tasks where the environment is constantly changing.
