14677592
Beschreibung
Karteikarten von Alberto Ochoa, aktualisiert more than 1 year ago
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Erstellt von Alberto Ochoa
vor fast 6 Jahre
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| Frage | Antworten |
| Parts of Animal Learning | Remembering, adapting, and generalising |
| Machine Learning Definition | Is making computers modify or adapt their actions so that these actions get more accurate, where accuracy is measured by how well the chosen actions reflect the correct ones. |
| Features | Picking the variables that you want to use |
| loosely define learning | as meaning getting better at some task through practice. |
| Supervised learning | A training set of examples with the correct responses (targets) is provided and, based on this training set, the algorithm generalises to respond correctly to all possible inputs. This is also called learning from exemplars. |
| Unsupervised learning | Correct responses are not provided, but instead the algorithm tries to identify similarities between the inputs so that inputs that have something in common are categorised together. The statistical approach to unsupervised learning is known as density estimation. |
| Reinforcement learning | This is somewhere between supervised and unsupervised learning. The algorithm gets told when the answer is wrong, but does not get told how to correct it. It has to explore and try out different possibilities until it works out how to get the answer right. Reinforcement learning is sometime called learning with a critic because of this monitor that scores the answer, but does not suggest improvements |
| Evolutionary learning | Biological evolution can be seen as a learning process: biological organisms adapt to improve their survival rates and chance of having offspring in their environment. We’ll look at how we can model this in a computer, using an idea of fitness, which corresponds to a score for how good the current solution is. |
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