Artificial intelligence

Which of the following is most likely used in deep learning?

(i) Decision Trees
(ii) Shallow neural networks
(iii) Multi-layered neural networks
(iv) K-Means algorithm

Neural Networks are inspired by

(i) Genetic evolution
(ii) Human brain
(iii) Particle movement
(iv) Graph theory

What do you mean by learning? Explain briefly the learning methods. Discuss the advantages and disadvantages of rule-based system.

Describe the following with suitable examples:

Logistic regression

Construct a simple decision tree from the given dataset:

Weather	Temp	Play?
Sunny	Hot	No
Sunny	Cool	Yes
Rainy	Cool	Yes
Rainy	Hot	No

Explain your attribute selection criteria.

Describe the following with suitable examples:

Back propagation algorithm

How can semi-supervised learning be useful in real-world scenarios like email spam detection? Describe its working with an example involving labeled and unlabeled data.

Discuss how backpropagation helps train a neural network. Use a small example with two layers and a loss function to explain weight adjustment.

Explain the difference between Supervised, Unsupervised, and Semi-Supervised Learning with examples.

What are the limitations of K-means clustering? Discuss two scenarios where K-means performs poorly and suggest possible improvements or alternatives.

Describe the architecture of a simple Artificial Neural Network. Explain the role of activation functions.

Describe logistic regression with suitable examples.

What is the goal of the support vector machine (SVM)? How to compute the margin?

Write a short note on:

Deep Learning