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Questions and Answers
What is the main goal of exploration in reinforcement learning?
What is the main goal of exploration in reinforcement learning?
What is the name of the approach to learning that is being described?
What is the name of the approach to learning that is being described?
What is the probability that A is a 'fast route'?
What is the probability that A is a 'fast route'?
What is the probability that A takes 30 minutes given that A is a 'fast route'?
What is the probability that A takes 30 minutes given that A is a 'fast route'?
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What is the formula used to update the probability that A is a 'fast route' given an observation?
What is the formula used to update the probability that A is a 'fast route' given an observation?
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What is the value of P(A is fast | observation) after observing that A takes 30 minutes?
What is the value of P(A is fast | observation) after observing that A takes 30 minutes?
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What percentage of games is similar to the example described?
What percentage of games is similar to the example described?
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What is the main challenge in learning in game theory?
What is the main challenge in learning in game theory?
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What is the main difference between reinforcement learning and game theory?
What is the main difference between reinforcement learning and game theory?
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What is the convenient approach for decision theory?
What is the convenient approach for decision theory?
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Study Notes
Classification and Hypothesis Development
- Classification aims to predict whether we will be paid back based on various attributes (a, c, i, e, o, u)
- Multiple hypotheses can be developed to predict payment, including:
- Income is high
- Income is high and no Criminal record
- Complex combinations of attributes (e.g., Address is known, NOT Old, OR Unemployed)
Nearest Neighbor Approach
- Nearest neighbor approach predicts payment based on the most similar example in the training data
- Distance is calculated between the new instance and each training instance
- The nearest neighbor's payment status is used for prediction
k-Nearest Neighbors Approach
- k-Nearest Neighbors (k-NN) approach predicts payment based on the k most similar examples in the training data
- A vote is taken among the k neighbors to determine the payment status
Perceptrons Approach
- Perceptrons approach uses weights to indicate the importance of each attribute in predicting payment
- Scores are calculated by summing the weighted attributes, and a threshold is set to determine payment status
Reinforcement Learning
- Reinforcement learning involves exploration vs. exploitation tradeoff
- Exploration involves trying new options, while exploitation involves sticking with known good options
- Reinforcement learning is often studied in Markov Decision Processes (MDPs)
Bayesian Approach to Learning
- Bayesian approach assumes a prior distribution over the long-term behavior of a route (e.g., A)
- The prior distribution is updated based on new observations, allowing for learning and adaptation
Learning in Game Theory
- Learning in game theory involves multiple agents learning simultaneously
- The environment is changing from one agent's perspective, making it challenging to make decisions
- Taking the average of past observations may not be the best approach in game theory
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Description
Determine a hypothesis to predict whether a loan will be paid back based on various factors such as income, criminal record, and more. Analyze the provided data to find the best predictor.
