mock exam - Ai

Give a complete problem formulation (Initial state, Goal test, Successor function, Cost function) for the following problem: "A 3-foot-tall monkey is in a room where some bananas are suspended from the 8-foot ceiling. He would like to get the bananas. The room contains two stackable, movable, climbable 3-foot-high crates."

Initial State: The initial state of the problem is that the monkey is on the ground, the bananas are suspended from the 8-foot ceiling, and the two stackable crates are in the room. Goal Test: The goal of the monkey is to reach the bananas. The goal test can be defined as the monkey successfully grabbing the bananas. Successor Function: The successor function describes how the state of the problem changes as the monkey takes actions. The monkey can either climb on one of the crates or move a crate to a new location. Therefore, the successor function generates new states by either moving the monkey up by one crate or moving a crate to a new location. Cost Function: The cost function represents the cost associated with each action. In this problem, each crate movement or climbing action can be assigned a cost of 1, as they require the same amount of effort. The cost of failure, i.e., not reaching the bananas, can be assigned a high value like 100 to discourage the agent from taking ineffective actions.

Which of the following is an example of an unsupervised learning algorithm: a. K-means clustering b. Decision trees c. Linear regression d. K-nearest neighbors

a. K-means clustering

Which of the following is NOT an application of Ai a. Social Media Marketing b. Weather forecasting c. Image recognition d. Speech recognition

a. Social Media Marketing

What is overfitting in machine learning? a. When the model is too complex and cannot generalize to new data b. When the model is too simple and does not capture the underlying pattern in the data c. When the model has too few features and cannot capture the complexity of the data d. When the model learns the noise in the training data instead of the underlying pattern

a. When the model is too complex and cannot generalize to new data

Which of the following PEAS description of the task environment and characteristics in terms of the properties are correct for "Bidding on an item at an auction" a. Fully observable, stochastic, sequential, static, continuous, single agent. O b. Fully observable, stochastic, episodic (every point is separate), dynamic, continuous, multi-agent. O c. Fully observable, strategic, sequential, static, discrete, multi-agent O d. Fully observable, deterministic, sequential, static, continuous, single agent

b. Fully observable, stochastic, episodic (every point is separate), dynamic, continuous, multi-agent.

Which of the following is not a type of artificial neural network? a. Recurrent Neural Network b. Multilayer Perception c. Backpropagation d. Convolutional Neural Network

c. Backpropagation.

What is the difference between supervised and unsupervised learning? a. In supervised learning, the input and output are both known, while in unsupervised learning, only the input is known. b. In unsupervised learning, the input and output are both known, while in supervised learning, only the input is known. c. In supervised learning, the input is known and the output is predicted, while in unsupervised learning, the input and output are both predicted. d. In unsupervised learning, the input is known and the output is predicted, while in supervised learning, the input and output are both predicted

c. In supervised learning, the input is known and the output is predicted, while in unsupervised learning, the input and output are both predicted.

Which of the following is a key challenge in natural language processing? a. Inability to handle large datasets b. Inaccuracy of speech recognition technology c. Lack of annotated data d. Limited computational power

c. Lack of annotated data.*

Are reflex actions (such as flinching from a hot stove) intelligent? a. Yes, intelligence is required to make a reflex action b. Yes, knowledge is being applied here c. None of them d. No, they are rational

c. None of them

Which of the following is not an ethical concern related to the use of Artificial Intelligence (Al)? a. Privacy b. Lack of transparency c. Security d. Bias

c. Security

Which of the following is NOT a type of machine learning? a. Unsupervised learning b. Reinforcement learning c. Semantic learning d. Supervised learning

c. Semantic learning

Explain the concept of transfer learning. How can you use transfer learning to improve the accuracy of a model?

Transfer learning is a learning approach in which a model that has been trained for one task is used as a [starting point] for a model that performs a similar task. It enables you to train models with less labeled data by reusing popular models that have already been trained on large datasets.- It can reduce training time and computing resources. Transfer learning can be used to improve the accuracy of a model by reusing the knowledge learned from a pre-trained model on a related task. This can be achieved through techniques such as feature extraction, fine-tuning, and multi-task learning, depending on the specific scenario. The approach is commonly used for object detection, image recognition, and speech recognition applications

Compare and contrast supervised and unsupervised learning. In what situations would you use each approach?

In supervised learning, the computer is given a set of labeled data and learns to predict the label of new, unseen data. In unsupervised learning, the agent learns input patterns without explicit feedback. Supervised learning is commonly used in applications such as image recognition, and speech recognition, while unsupervised learning is commonly used in applications such as recommendation systems, and data exploration.

What is the purpose of a validation set in machine learning?

The purpose of a validation set is to prevent overfitting, which occurs when the model performs well on the training set but poorly on new data. By evaluating the model on a validation set, we can detect overfitting early and adjust the model or its hyperparameters to improve its generalization performance. the validation set is used to tune hyperparameters and optimize the model during the training process. After each epoch of training, the model is evaluated on the validation set to check its performance. This allows us to compare different models and hyperparameters to choose the best performing one.

Briefly explain the concept of backpropagation in neural networks.

a popular supervised learning algorithm used in training feedforward neural networks, including Multi-Layer Perceptrons (MLPs). The algorithm works by propagating the error or loss between the predicted output and the actual output backwards through the network to adjust the weights of the connections between the neurons.