Exploring Machine Learning and Networks

What is supervised learning in machine learning?

• Supervised learning is a machine learning approach that uses labeled data to train models to make predictions.

• Supervised learning is a method that requires no data for training.

• Unsupervised learning uses labeled data to train models.

• Supervised learning focuses solely on clustering data without labels.

Name two common algorithms used in supervised learning.

• Neural Networks

• Decision Trees, Support Vector Machines (SVM)

• K-Means Clustering

• Principal Component Analysis

What is the difference between classification and regression?

• Classification predicts future values; regression classifies data points.

• Classification deals with categorical outcomes; regression deals with continuous outcomes.

• Classification is used for time series analysis; regression is for clustering.

• Classification uses linear equations; regression uses decision trees.

Define overfitting in the context of machine learning.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.

• Overfitting refers to a model that generalizes well to new data but fails on training data.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

What is a training set and a test set?

• A training set is a collection of test results, while a test set is a model's parameters.

• A training set is used for training a model, while a test set is used for evaluating its performance.

• A training set is used for evaluating a model, while a test set is used for training.

• A training set is used to store data, while a test set is used to generate new data.

Explain the concept of network topology.

• Network topology is a type of network security protocol.

• Network topology refers to the physical location of network devices only.

• Network topology is the arrangement of elements in a network, defining how devices are interconnected.

• Network topology is the speed at which data travels through a network.

What are the main types of network topologies?

• Square

• Star, Bus, Ring, Mesh, Tree, Hybrid

• Circle

• Line

Describe the star topology and its advantages.

• The star topology is the most complex network design.

• In star topology, if the central hub fails, the entire network remains operational.

• Star topology requires all devices to be connected in a single line.

• The star topology offers advantages such as easy installation and configuration, centralized management, and high fault tolerance since failure of one device does not impact the entire network.

What is a disadvantage of a bus topology?

• Each device has its own dedicated cable for connection.

• Data transmission is faster than in a star topology.

• The entire network can fail if the main cable fails.

• It is easy to add new devices without disruption.

How does a mesh topology differ from a star topology?

• A mesh topology uses a single cable to connect all devices.

• A star topology has direct connections between all devices.

• A mesh topology requires fewer cables than a star topology.

• A mesh topology has direct connections between all devices, while a star topology connects devices through a central hub.