TRUE/FALSE QUESTIONS

The letter ‘k’ in K-Means represents the total number of clusters defined in the algorithm.

During backpropagation, weight updates occur in the forward pass.

Bias neurons are only added to the input layer in an MLP.

A multi-layer perceptron requires a minimum of one hidden layer to function.

In a perceptron, the bias is multiplied by an input.

Fuzzy systems use Boolean logic (0 or 1) for decision-making.

Jaccard similarity is used for numeric data.

The backpropagation algorithm relies on the chain rule of calculus to determine gradient values for weight updates.

A single-layer perceptron can classify non-linearly separable data.

In K-Means clustering, centroids are recalculated after every iteration using the points assigned to each cluster.

Each connection between neurons in an MLP has a weight that is learned during training.

In K-Means, the centroid of a cluster is computed as the median, of its data points.

A single-layer perceptron includes hidden layers.

Computational Intelligence (CI) is a subset of Artificial Intelligence (AI).

The Turing Test is designed to assess a machine's physical strength.

ANNs can be trained using both supervised and unsupervised learning.

Clustering requires labeled data for training.

Neural networks are inspired by the human brain's structure and function.

A step activation function is typically applied in a single-layer perceptron model.

In artificial neural networks, a neuron activates only when its input surpasses a certain threshold value.