ch18

Question 1

Machine learning

Answer 1

systems that learn without being
programmed to learn

Question 2

Deep learning

Answer 2

machines that think in a way similar to the human brain. They handle huge amounts of data using artificial neural networks/////
* Uses artificial neural network(s)
* … that contain(s) a high number of hidden layers
* … modelled on the human brain.

Question 3

relation between machine learning and deep learning

Answer 3

Deep learning is a subset of machine learning, which is itself a subset of AI

Question 4

AI can be split into 3 main categories what are they

Answer 4

1. Narrow AI: is when a machine has superior performance to a human when doing one specific task.
2. General AI: is when a machine is similar in its performance to a human in any intellectual task.
3. Strong AI: is when a machine has superior performance to a human in many tasks

Question 5

Labelled data

Answer 5

data where we know the target answer and the data object is fully recognised

Question 6

Unlabelled data

Answer 6

data where objects are undefined and need to be manually recognised

Question 7

Supervised learning

Answer 7

system which is able to predict future outcomes based on past data. It requires both input and output values to be used in the training process////
* Supervised learning allows data to be collected, or a data output produced, from the previous experience.
* In supervised learning, known input and associated outputs are given // uses sample data with known outputs (in
training) // uses labelled input data.
* Able to predict future outcomes based on past data.

Question 8

Unsupervised learning

Answer 8

system which is able to identify hidden patterns from input data – the system is not trained on the ‘right’ answer. the algorithm is given input data without explicit instructions on what to do with it. In unsupervised learning, the system tries to learn the patterns and the structure inherent in the data without explicit guidance in the form of labeled outputs or target variables./////
* Unsupervised machine learning helps all kinds of unknown patterns in data to be found.
* Unsupervised learning only requires input data to be given.
* Uses any data // not trained on the right output

Question 9

Reinforcement learning

Answer 9

system which is given no training – learns on basis of ‘reward and punishment’.

Question 10

Semi-supervised (active) learning

Answer 10

system that interactively queries source data to reach the desired
result. It uses both labelled and unlabelled data, but mainly unlabelled data on cost grounds.

Question 11

analysis used in supervised learning

Answer 11

Supervised learning makes use of regression analysis and classification analysis.

Question 12

Regression analysis

Answer 12

statistical technique used in data analysis to model and examine the relationship between a dependent variable and one or more independent variables. The main goal of regression analysis is to understand how the dependent variable changes as the independent variables vary. This analysis helps in predicting the value of the dependent variable based on the values of the independent variables//// statistical measure used to make
predictions from data by finding learning relationships
between the inputs and outputs.

Question 13

Data mining

Answer 13

the process of discovering patterns, trends, correlations, or valuable insights from large sets of data. It involves extracting useful and meaningful information from vast and often complex datasets. Data mining is a crucial aspect of artificial intelligence (AI) because it enables machines to learn and make predictions based on historical data.

Question 14

Classification analysis

Answer 14

type of statistical and machine learning technique used to categorize data into predefined classes or groups based on the characteristics of the input variables. The primary goal of classification is to build a model that can learn from labeled training data and make predictions or decisions about the class labels of new, unseen data.

Question 15

more about supervised learning

Answer 15

» The system requires both an input and an output to be given to the model
so it can be trained.
» The model uses labelled data, so the desired output for a given input is known.

» Algorithms receive a set of inputs and the correct outputs to permit the learning process.

» Once trained, the model is run using labelled data.

» The results are compared with the expected output; if there are any errors, the model needs further refinement.

» The model is run with unlabelled data to predict the outcome.

Question 16

Reward and punishment

Answer 16

improvements to a model
based on whether feedback is positive or negative; actions
are optimised to receive an increase in positive feedback.

Question 17

how does Reinforcement learning learn

Answer 17

on the basis of ‘reward and
punishment’

Question 18

Web crawler

Answer 18

internet bot that systematically browses the world wide web to update its web page content.

Question 19

how does deep learning structures algorithms

Answer 19

in layers (input layer, output layer and hidden layer(s))

Question 20

Artificial neural networks

Answer 20

networks of interconnected nodes based on the interconnections between neurons in the human brain. The system is able to think like a human using these neural networks, and its performance improves with more data.

Question 21

Back propagation

Answer 21

method used in artificial neural
networks to calculate error gradients so that actual node/neuron weightings can be adjusted to improve the
performance of the model. It involves feeding input data forward through the network to produce an output, calculating the error between the predicted output and the actual target values, and then iteratively adjusting the weights of the network in the reverse direction (backward) to minimize the error. This process is repeated through multiple iterations or epochs until the network learns to make accurate predictions on new, unseen data.

Question 22

summary of the back propagation process

Answer 22

» The initial outputs from the system are compared to the expected outputs and the system weightings are adjusted to minimise the difference between actual and expected results.

» Calculus is used to find the error gradient in the obtained outputs: the results are fed back into the neural networks and the weightings on each neuron are adjusted (note: this can be used in both supervised and unsupervised networks).

» Once the errors in the output have been eliminated (or reduced to acceptable limits) the neural network is functioning correctly and the model has been successfully set up.

» If the errors are still too large, the weightings are altered – the process continues until satisfactory outputs are produced

Question 23

two types of back propagation

Answer 23

static and recurrent:
» Static maps static inputs to a static output.

» Mapping is instantaneous in static, but this is not the case with recurrent.

» Training a network/model is more difficult with recurrent than with static.

» With recurrent, activation is fed forward until a fixed value is achieved.

Question 24

Outline the reasons for using deep learning. (2)

Answer 24

Deep learning makes good use of unstructured data.
* Deep learning outperforms other methods if the data size is large.
* Deep learning systems enable machines to process data with a nonlinear approach
* It can provide a more accurate outcome with higher numbers of hidden layers.

Question 25

Explain how artificial neural networks enable machine learning. (4)

Answer 25

• Artificial neural networks are intended to replicate the way human brains
  work
• Weights / values are assigned for each connection between nodes
• The data are input at the input layer and are passed into the system
• They are analysed at each subsequent (hidden) layer where
  characteristics are extracted / outputs are calculated
• … this process of training / learning is repeated many times to achieve
  optimum outputs // reinforcement learning takes place

Question 26

State the reason for having multiple hidden layers in an artificial neural network. (1)

Answer 26

• Enables deep learning to take place
• Where the problem you are trying to solve has a higher level of
  complexity it requires more layers to solve
• To enable the neural network to learn and make decisions on its own
• To improve the accuracy of the result.