AI Flashcards
Explain Supervised Learning
Enables learning by mapping an input to an output based on example input–output pairs.
Exaplin reinforcement learning
-Using a large number of tasks with unknown outcomes and use of feedback to enable a computer program to improve its performance in accomplishing similar tasks.
Explain deep learning.
Uses artificial neural network(s)
… that contain(s) a high number of hidden layers
… modelled on the human brain
- Deep learning uses many layers to progressively extract higher level features from the (raw) input.
-Deep learning is a specialised form of machine learning
Explain Unsupervised Learning.
Enables learning by allowing the process to discover patterns on its own that were previously undetected.
Describe the purpose of both the A* algorithm and Dijkstra’s algorithm.
-Find the optimal route between two nodes based on distance/time/cost.
What is a graph in AI
-A graph is a collection of nodes or vertices between which there can be edges.
-Each node has a name.
-An edge can have an associated label which is a numerical value.
Explain the use of graphs to aid in AI.
-Artificial neural networks can be represented by graphs.
-Graphs provide structure for relationship // graphs provide relationships between nodes.
-AI problem can be defined as finding a path in a graph
-Graph may be analysed/injected by a range of algorithms.
… A* /Dijkstra’s algorithm
… used in machine learning
-Back propagation of errors / regressions.
What is a neural network
-An artificial neural network is the component of artificial intelligence that is meant to simulate the functioning of a biological brain.
-Artificial neural networks are a key component of machine learning.
-They can solve problems that would prove impossible or difficult for humans // Artificial neural networks have self-learning capabilities that enable them to produce better results as more data becomes available.
-Artificial neural networks can be layered (input, hidden and output layers) // Artificial neural networks have many interconnected layers, some / many of which are hidden.
-Weights are assigned between nodes MP6 Weights are adjusted through training to give a more accurate result.
-More complex learning capabilities / more accurate results are available with larger numbers of hidden layers
State the reasons for having multiple hidden layers in an artificial neural network.
-Enable deep learning to take place.
-Where the problem you are trying to solve has a higher level of complexity it requires more layers.
-Allow neural network to learn and make decisions on its own.
Explain how artificial neural networks enable machine learning.
-Artificial neural networks are intended to replicate the way the human brain works.
-Weights / values are assigned for each connection between nodes.
-The data are input at the input layer, then passed into the system.
-THey are analysed at each subsequent hidden layer where characteristics are extracted / output are calculated.
-This process of training / learning is repeated many times to achieve optimum outputs // reinforcement learning takes place.
-Decisions can be made without being specifically programmed.
-The output layer provides the result.
-Back propagation of errors will be used to correct any errors that have been made.
Expalin regression analysis
-Finding a mathematical function that provides the best fit to the actual outcomes when outcomes are calculated from previous inputs. Allows you to predict a dependent output variable based on the values of independent input variables.
Explain back propagation of errors.
-BAck propagation of errors, is an algorithm that is designed to test for errors working back from output nodes through the nodes in the hidden layers until finally the input nodes.
-It is done before a neural network is ready to be deployed in the field. You use the training data, which already has known results, to perform backpropagation.
