ML 2

Targetted marketing, Recommended Systems, and Customer Segmentation are applications in which of the following

• Supervised Learning: Classification

• Unsupervised Learning: Clustering

• Unsupervised Learning: Regression

• Reinforcement Learning 

Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in which of the following

• Unsupervised Learning: Regression

• Supervised Learning: Classification

• Unsupervised Learning: Clustering

• Reinforcement Learning 

What is perceptron?

• A single layer feed-forward neural network with pre-processing

• A neural network that contains feedback

• A double layer auto-associative neural network

• An auto-associative neural network

Which is true for neural networks?

• (a) Each node computes it‟s weighted input

• (b) It has set of nodes and connections

• (c) All nodes are in non-excited state

• both the (a) and (b)

What is the objective of backpropagation algorithm?

• To develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly

• To develop learning algorithm for single layer feedforward neural network

• To develop learning algorithm for 2-layer feedforward neural network

• All of the above 

What is back propagation?

• It is another name given to the curvy function in the perceptron

• It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn

• It is another name given to the curvy function in the perceptron

• None of the above

A learning method in which a training data contains a small amount of labeled data and a large amount of unlabeled data is known as ___.

• Supervised Learning

• Semi Supervised Learning

• Unsupervised Learning

• Reinforcement Learning 

The most common issue when using ML is

• Lack of skilled resources

• Inadequate Infrastructure

• Poor Data Quality

• None of these 

Common classes of problems in machine learning is ___.

• Classification

• Clustering

• Regression

• All of these

When you find noise in data which of the following option would you consider in K- Nearest Neighbor?

• I will increase the value of k

• I will decrease the value of k

• Noise cannot be dependent on value of k

• None of these