BNF43603 Machine Learning Quiz

What is the main goal of reinforcement learning?

• To train an agent to make sequential decisions in an environment in order to maximize a cumulative reward.

• To minimize the cumulative reward

• To avoid making sequential decisions

• To ignore the environment

Which type of neural network is commonly used in deep learning?

• CNNs

• RNNs

• SVMs

• Decision Trees

What is the primary purpose of genetic algorithms in machine learning?

• To create random solutions without optimization

• To optimize and find the best solutions for complex problems

• To make problems more complex

• To slow down the process of finding solutions

What is the main principle behind particle swarm optimization?

• Isolation and independence

• Social behavior and collaboration

• Random guessing

• Individual competition

In reinforcement learning, what is the role of the agent?

• Take actions without interacting with the environment

• Sit back and observe the environment

• Interact with the environment, take actions, and receive rewards or penalties

• Ignore the rewards and penalties

What is the purpose of activation functions in deep learning?

• To make the output of a neuron linear

• To introduce non-linearity into the output of a neuron

• To slow down the learning process

• To reduce the accuracy of the model

How does genetic algorithm create new generations of solutions?

• Mutation, selection, and duplication

• Random guessing, recombination, and mutation

• Selection, crossover, and mutation

• Sorting, crossover, and duplication

What are the key components of a deep learning neural network?

• The key components of a deep learning neural network are input layer, hidden layers, output layer, weights, biases, activation functions, and the gain function.

• The key components of a deep learning neural network are input layer, hidden layers, output layer, weights, biases, activation functions, and the loss function.

• The key components of a deep learning neural network are input layer, hidden layers, output layer, weights, biases, activation functions, and the error function.

• The key components of a deep learning neural network are input layer, hidden layers, output layer, weights, biases, activation functions, and the gain function.

What are the advantages of using particle swarm optimization in optimization problems?

• It is a slow and inefficient method

• It can efficiently search for optimal solutions in a large search space.

• It only works for small search spaces

• It is not suitable for finding optimal solutions

How does reinforcement learning differ from supervised learning?

• Reinforcement learning does not involve trial and error

• Reinforcement learning uses labeled data

• Reinforcement learning involves learning from a reward signal.

• Reinforcement learning does not involve decision making