A-LevelComputer ScienceArtificial intelligence9618/31 · May/June 2023 · Q26 Marks
Draw one line from each machine learning category to its most appropriate description. Machine learning category Description Supervised learning simulates the data-processing capabilities of the human brain to make decisions Reinforcement learning enables learning by mapping an input to an output based on example input- output pairs Deep learning enables information related to errors produced by the neural network to be transmitted Unsupervised learning enables learning in an interactive environment by trial and error using its own experiences enables learning by allowing the process to discover patterns on its own that were previously undetected
- (a)Draw one line from each machine learning category to its most appropriate description. Machine learning category Description Supervised learning simulates the data-processing capabilities of the human brain to make decisions Reinforcement learning enables learning by mapping an input to an output based on example input- output pairs Deep learning enables information related to errors produced by the neural network to be transmitted Unsupervised learning enables learning in an interactive environment by trial and error using its own experiences enables learning by allowing the process to discover patterns on its own that were previously undetected[4]
- (b)Describe the purpose of both the A* algorithm and Dijkstra's algorithm.[2]
Mark scheme
Marking points from the official mark scheme (May/June 2023). The mark or mark type is shown in brackets.
- [4] (a) One mark for each correct line connecting a machine learning technique to its most appropriate description (Max 4). Supervised learning -> enables learning by mapping an input to an output based on example input-output pairs Reinforcement learning -> enables learning in an interactive environment by trial and error using its own experiences Deep learning -> simulates the data-processing capabilities of the human brain to make decisions Unsupervised learning -> enables learning by allowing the process to discover patterns on its own that were previously undetected
- [2] (b) One mark per mark point (Max 2) • to find the optimal / shortest / most cost-effective route • between two nodes in a • based on distance / cost / time.
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