Markov Decision Process (MDP) Quiz
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Questions and Answers

Who is Markov associated with in the context of decision-making under uncertainty?

  • An American economist who introduced the concept of utility functions
  • A British philosopher who coined the term 'decision theory'
  • A German engineer who designed the first autonomous robot
  • A Russian mathematician who developed a theory of stochastic processes (correct)

    • What type of process is used to model decision-making under uncertainty in Markov Decision Processes?

  • Linear process
  • Stochastic process (correct)
  • Deterministic process
  • Dynamic process

    • What is the key characteristic of Markov Decision Processes that allows them to handle uncertainty?

  • They rely on human intuition to make decisions
  • They use Bayesian networks to model uncertainty
  • They assume a fixed probability distribution over outcomes
  • They use probabilistic transitions to model uncertainty (correct)

    • What is a fundamental characteristic of a Markovian system?

    <p>The future does not depend on the past given the present.</p> Signup and view all the answers

    In the context of Markov Decision Processes, what is the goal of the decision-making process?

    <p>To maximize the expected reward of taking an action</p> Signup and view all the answers

    What is the purpose of the Transition Function in a Markov Decision Process (MDP)?

    <p>To define the probability of moving from one state to another given an action.</p> Signup and view all the answers

    What is the role of the Reward Function in a Markov Decision Process (MDP)?

    <p>To give the immediate reward (or penalty) received after transitioning from one state to another via an action.</p> Signup and view all the answers

    What is the relationship between Markov Decision Processes and planning?

    <p>Markov Decision Processes are used to plan under uncertainty</p> Signup and view all the answers

    What is a component of a Markov Decision Process (MDP) that provides the agent with complete information about the past relevant to future decisions?

    <p>States (S)</p> Signup and view all the answers

    What is an essential aspect of a Markov Decision Process (MDP) that makes it suitable for addressing reinforcement learning (RL) problems?

    <p>The ability to model partly random and partly controllable outcomes.</p> Signup and view all the answers

    What does the Markov property imply about predicting the future?

    <p>You need to know the current state and the action taken in that state.</p> Signup and view all the answers

    What is the key difference between Markovian and non-Markovian processes?

    <p>The dependence on the entire history of past states and actions.</p> Signup and view all the answers

    What is the practical implication of a state being Markovian?

    <p>The current state encapsulates all relevant information from the past.</p> Signup and view all the answers

    In a Markovian process, what does the probability of transitioning to the next state depend on?

    <p>The current state and the action taken in that state.</p> Signup and view all the answers

    What is the consequence of a process being non-Markovian?

    <p>The entire history of past states and actions must be kept track of.</p> Signup and view all the answers

    What is the primary objective of an agent in a Markov Decision Process?

    <p>To maximize the cumulative reward over time</p> Signup and view all the answers

    What type of reward is given intermittently in a Markov Decision Process?

    <p>Sparse reward</p> Signup and view all the answers

    What is the specific notation for the reward function in a Markov Decision Process?

    <p>R(s, a, s')</p> Signup and view all the answers

    What is the effect of a positive reward on an agent's behavior in a Markov Decision Process?

    <p>It incentivizes the agent to take actions</p> Signup and view all the answers

    What is the impact of a well-designed reward function on an agent's learning and performance in a Markov Decision Process?

    <p>It has a significant impact</p> Signup and view all the answers

    What is the primary goal when solving an MDP?

    <p>To find an optimal policy that maximizes the cumulative reward</p> Signup and view all the answers

    What is the purpose of heuristic search in solving MDPs?

    <p>To focus computational efforts on the most promising parts of the state space</p> Signup and view all the answers

    What is the primary benefit of using Value Iteration in MDPs?

    <p>It enables faster convergence on effective policies</p> Signup and view all the answers

    What is typically done to the state values in the initialization step of Value Iteration?

    <p>They are set to zero</p> Signup and view all the answers

    Which algorithm combines heuristic estimates of future state values with immediate rewards to choose actions?

    <p>Greedy Algorithm</p> Signup and view all the answers

    What is the primary purpose of the discount factor in the Bellman equation?

    <p>To balance the trade-off between immediate and future rewards</p> Signup and view all the answers

    What is the primary advantage of using Policy Iteration to solve Markov Decision Processes?

    <p>It ensures that the derived policy maximizes the total expected return from any given state</p> Signup and view all the answers

    What is the purpose of the policy evaluation step in Policy Iteration?

    <p>To compute the value of each state under the current policy</p> Signup and view all the answers

    What is the condition for terminating the iteration process in Policy Iteration?

    <p>The change in values between iterations falls below a predefined small threshold</p> Signup and view all the answers

    What is the primary difference between the Bellman equation and Policy Iteration?

    <p>The Bellman equation is used for policy evaluation, while Policy Iteration is used for policy improvement</p> Signup and view all the answers

    What is the primary purpose of reward shaping in Markov Decision Processes?

    <p>To make desired outcomes more apparent and immediate</p> Signup and view all the answers

    What is the main challenge associated with designing a reward function in Markov Decision Processes?

    <p>The difficulty in linking delayed rewards to specific actions</p> Signup and view all the answers

    What is the purpose of a living reward or living cost in Markov Decision Processes?

    <p>To incentivize or penalize certain behaviours</p> Signup and view all the answers

    What is the characteristic of the transition function in a Markov Decision Process?

    <p>It is stochastic and typically probabilistic</p> Signup and view all the answers

    What is the primary component of a Markov Decision Process that captures the uncertainty and variability of the environment?

    <p>The transition function</p> Signup and view all the answers

    What is the sequence of rewards in a Markov Decision Process?

    <p>The series of rewards an agent collects over time</p> Signup and view all the answers

    What is the primary role of the reward structure in guiding agent behaviour in Markov Decision Processes?

    <p>To guide agent behaviour towards achieving set objectives</p> Signup and view all the answers

    What is the consequence of a poorly designed reward function in Markov Decision Processes?

    <p>The agent will have difficulty learning the optimal policy</p> Signup and view all the answers

    What is the primary advantage of using a living reward or living cost in Markov Decision Processes?

    <p>It provides more immediate feedback to the agent</p> Signup and view all the answers

    What is the relationship between the reward function and the sequence of rewards in Markov Decision Processes?

    <p>The reward function determines the sequence of rewards</p> Signup and view all the answers

    What is the primary role of prior knowledge in Explanation-Based Learning?

    <p>To reduce the complexity of learning by providing a framework for understanding</p> Signup and view all the answers

    What is the main difference between Memorization and Explanation-Based Learning?

    <p>Memorization accumulates a database of input–output pairs, while EBL extracts general rules</p> Signup and view all the answers

    What is the purpose of the generalized proof tree in Explanation-Based Learning?

    <p>To construct a new rule whose left-hand side consists of the leaves of the proof tree</p> Signup and view all the answers

    What is the primary benefit of using Explanation-Based Learning?

    <p>It can create general rules that cover an entire class of cases</p> Signup and view all the answers

    What is the relationship between Inductive Logic Programming (ILP) and Knowledge-Based Inductive Learning (KBIL)?

    <p>ILP is a subset of KBIL</p> Signup and view all the answers

    What is the primary goal of learning by extension of the goal predicate?

    <p>To extend the goal predicate to include false negative examples</p> Signup and view all the answers

    What is the characteristic of Knowledge-based learning?

    <p>It involves learning by ruling out wrong hypotheses</p> Signup and view all the answers

    What is the consequence of a false positive example in Knowledge-based learning?

    <p>The hypothesis is specialized to exclude the example</p> Signup and view all the answers

    What is the primary advantage of Support Vector Machines over deep learning networks and random forests?

    <p>They construct a maximum margin separator</p> Signup and view all the answers

    What is the primary goal of learning by searching for the current-best-hypothesis?

    <p>To adjust a single hypothesis to maintain consistency with new examples</p> Signup and view all the answers

    What type of learning is characterized by the ability to predict the appearance of a particular object, class, or pattern?

    <p>Prediction</p> Signup and view all the answers

    What is the primary role of background knowledge in relevance-based learning?

    <p>To identify relevant attributes</p> Signup and view all the answers

    What is the primary goal of supervised learning?

    <p>To learn a function that maps from input to output</p> Signup and view all the answers

    What is the characteristic of the learning process in knowledge-based inductive learning?

    <p>Deductive form of learning</p> Signup and view all the answers

    What is the primary characteristic of unsupervised learning?

    <p>Processing data input to learn patterns without explicit feedback</p> Signup and view all the answers

    What is the purpose of the hypothesis in supervised learning?

    <p>To approximate the true function that maps from input to output</p> Signup and view all the answers

    What is the primary goal of knowledge-based inductive learning?

    <p>To explain sets of observations</p> Signup and view all the answers

    What is the key limitation of knowledge-based inductive learning?

    <p>It cannot create new knowledge</p> Signup and view all the answers

    What is the primary difference between supervised and unsupervised learning?

    <p>Supervised learning involves explicit feedback, while unsupervised learning does not</p> Signup and view all the answers

    What is the benefit of using prior knowledge in relevance-based learning?

    <p>To identify relevant attributes</p> Signup and view all the answers

    What is a key difference between Reflex Agents with State and Model-Based Reflex Agents?

    <p>The ability to learn from experience</p> Signup and view all the answers

    Which type of agent relies on pre-defined rules provided by programmers or designers?

    <p>Simple Reflex Agents</p> Signup and view all the answers

    What is a key characteristic of Reflex Agents with State?

    <p>They maintain an internal state representation of the world</p> Signup and view all the answers

    What enables Model-Based Reflex Agents to make more sophisticated decisions?

    <p>The internal state representation of the world</p> Signup and view all the answers

    What is a common limitation of Simple Reflex Agents?

    <p>They cannot learn from experience</p> Signup and view all the answers

    What is a key concept in explanation-based learning?

    <p>Generalization</p> Signup and view all the answers

    What is the primary purpose of generalization in learning from examples?

    <p>To find a definition C1 that is logically implied by C2</p> Signup and view all the answers

    What is the role of knowledge in the modern approach to AI?

    <p>To design agents that already know something about the solution and are trying to learn more</p> Signup and view all the answers

    What is the primary benefit of explanation-based learning?

    <p>Ability to learn from a single example</p> Signup and view all the answers

    What is the relationship between specialization and generalization in learning from examples?

    <p>Generalization is a logical relationship between hypotheses, where a hypothesis h1 is a generalization of hypothesis h2 if ∀ x C2(x) ⇒ C1(x)</p> Signup and view all the answers

    What is the primary goal of the learning agent in minimizing the expected loss?

    <p>To minimize the loss function</p> Signup and view all the answers

    What is the key characteristic of parametric models?

    <p>They can be characterized by a bounded set of parameters</p> Signup and view all the answers

    What is the main difference between parametric and non-parametric models?

    <p>Parametric models are characterized by a bounded set of parameters, while non-parametric models are not</p> Signup and view all the answers

    What is an example of a non-parametric learning method?

    <p>Table lookup</p> Signup and view all the answers

    What is the purpose of k-fold cross-validation?

    <p>To perform k rounds of learning on each round of the data</p> Signup and view all the answers

    What is the criterion for selecting a hypothesis in learning from examples?

    <p>To minimize the loss function</p> Signup and view all the answers

    What is the relationship between the loss function and the utility function?

    <p>The loss function is the opposite of the utility function</p> Signup and view all the answers

    What is the primary advantage of using k-fold cross-validation?

    <p>It provides a more accurate estimate of the model's performance</p> Signup and view all the answers

    What is the purpose of the validation set in k-fold cross-validation?

    <p>To evaluate the model's performance</p> Signup and view all the answers

    What is the main difference between a parametric and non-parametric model in terms of the number of parameters?

    <p>Parametric models have a fixed number of parameters, while non-parametric models have a variable number of parameters</p> Signup and view all the answers

    What is the primary role of background knowledge in explanation-based learning?

    <p>To reduce the complexity of learning by providing general rules</p> Signup and view all the answers

    What is the main difference between memorization and explanation-based learning?

    <p>Memorization stores individual observations, while explanation-based learning creates general rules</p> Signup and view all the answers

    What is the primary goal of knowledge-based inductive learning?

    <p>To extend background knowledge over time through learning</p> Signup and view all the answers

    What is the purpose of the generalized proof tree in explanation-based learning?

    <p>To create general rules that cover an entire class of cases</p> Signup and view all the answers

    What is the relationship between inductive logic programming and knowledge-based inductive learning?

    <p>Inductive logic programming is a type of knowledge-based inductive learning</p> Signup and view all the answers

    What occurs when a hypothesis predicts that a set of examples will be examples of the goal predicate?

    <p>The hypothesis is extended to include the examples</p> Signup and view all the answers

    What is the outcome when there is a new example that is a false positive in knowledge-based learning?

    <p>The hypothesis is specialized to exclude the example</p> Signup and view all the answers

    What is the primary goal of learning by searching for the current-best-hypothesis?

    <p>To maintain a single hypothesis and adjust it as new examples arrive</p> Signup and view all the answers

    What is a key characteristic of knowledge-based learning?

    <p>It involves learning from examples and background knowledge</p> Signup and view all the answers

    What occurs when there is a new example that is a false negative in knowledge-based learning?

    <p>The hypothesis is generalized to include the example</p> Signup and view all the answers

    What is the primary role of background knowledge in relevance-based learning?

    <p>To provide prior knowledge in the form of determinations</p> Signup and view all the answers

    What is the characteristic of the learning process in knowledge-based inductive learning?

    <p>It relies on the agent's prior knowledge</p> Signup and view all the answers

    What is the primary goal of the agent in knowledge-based inductive learning?

    <p>To formulate a hypothesis that explains the observations</p> Signup and view all the answers

    What is the key feature of relevance-based learning?

    <p>It uses the goal predicate to identify relevant features</p> Signup and view all the answers

    What is the primary limitation of knowledge-based inductive learning?

    <p>It cannot create new knowledge from scratch</p> Signup and view all the answers

    What is the main purpose of supervised learning?

    <p>To establish a function that maps inputs to outputs</p> Signup and view all the answers

    What is the key characteristic of unsupervised learning?

    <p>Processing data inputs without explicit feedback</p> Signup and view all the answers

    What is the primary goal of identification in machine learning?

    <p>To unambiguously recognize an item based on unique attributes</p> Signup and view all the answers

    What is the role of a hypothesis in supervised learning?

    <p>To approximate the true function</p> Signup and view all the answers

    What is the relationship between the training set and the hypothesis in supervised learning?

    <p>The hypothesis must be consistent with the training set</p> Signup and view all the answers

    Which type of agent can adapt to changes in the environment by updating their internal models and adjusting their behavior accordingly?

    <p>Model-Based Reflex Agent</p> Signup and view all the answers

    What is necessary for a hypothesis h to be a generalization of another hypothesis h2?

    <p>∀ x C2(x) ⇒ C1(x)</p> Signup and view all the answers

    Which of the following is a characteristic of Reflex Agents with State?

    <p>They maintain an internal state representation of the world</p> Signup and view all the answers

    What are the two properties required for the general structure of the boundary-set to be sufficient for representing the version space?

    <p>Every hypothesis more specific than some member of the G-set and more general than some member of the S-set is a consistent hypothesis.</p> Signup and view all the answers

    What is a key difference between Reflex Agents with State and Model-Based Reflex Agents?

    <p>Their incorporation of learning algorithms</p> Signup and view all the answers

    Which type of agent relies on pre-defined rules provided by programmers or designers?

    <p>Reflex Agent</p> Signup and view all the answers

    What is the primary goal of Explanation-Based Learning (EBL) in a learning process?

    <p>To extract general rules from a single example</p> Signup and view all the answers

    What is the relationship between specialization and generalization in learning from examples?

    <p>Specialization is the opposite of generalization</p> Signup and view all the answers

    What is a key characteristic of Model-Based Reflex Agents?

    <p>They select actions based on both the current percept and the internal state representation</p> Signup and view all the answers

    What is the role of knowledge in the modern approach to AI?

    <p>To design agents that know something about the solution and are trying to learn more</p> Signup and view all the answers

    What is the primary goal of the learning agent in minimizing the loss function?

    <p>To choose the hypothesis that minimizes expected loss</p> Signup and view all the answers

    What is the key characteristic of non-parametric models?

    <p>They can be characterized by a bounded set of parameters</p> Signup and view all the answers

    What is the purpose of k-fold cross-validation in learning from examples?

    <p>To perform k rounds of learning on each round with a different validation set</p> Signup and view all the answers

    What is the consequence of a poorly designed loss function in learning from examples?

    <p>The learning agent may not minimize the expected loss</p> Signup and view all the answers

    What is the primary advantage of using parametric models in learning from examples?

    <p>They can be characterized by a bounded set of parameters</p> Signup and view all the answers

    What is the purpose of the lookup table in non-parametric learning?

    <p>To take all the training examples and return the corresponding output</p> Signup and view all the answers

    What is the primary goal of the learning agent in knowledge-based learning?

    <p>To use prior knowledge to guide the learning process</p> Signup and view all the answers

    What is the consequence of a false positive example in knowledge-based learning?

    <p>The learning agent may not minimize the expected loss</p> Signup and view all the answers

    What is the key difference between parametric and non-parametric models?

    <p>The number of parameters used to summarize the data</p> Signup and view all the answers

    What is the primary role of the hypothesis in learning from examples?

    <p>To predict the correct answer</p> Signup and view all the answers

    What is a potential consequence of AI systems perpetuating biases present in their training data?

    <p>Unfair treatment of certain groups</p> Signup and view all the answers

    What is a key challenge in determining the ownership of AI-generated content or inventions?

    <p>Lack of clear legal frameworks</p> Signup and view all the answers

    What is a potential consequence of over-reliance on AI in various sectors?

    <p>Dehumanization in various sectors</p> Signup and view all the answers

    What is a key approach to limiting the impact of AI systems on privacy violations?

    <p>Implementing rigorous ethical guidelines</p> Signup and view all the answers

    What is a potential legal challenge in assigning liability when AI systems cause harm or damage?

    <p>Assigning liability to the AI system itself</p> Signup and view all the answers

    What is a key benefit of establishing clear legal frameworks for AI systems?

    <p>Clear definition of rights and responsibilities associated with AI outputs</p> Signup and view all the answers

    What is a key approach to addressing the issue of bias in AI systems?

    <p>Regularly auditing AI systems for bias and compliance with privacy laws</p> Signup and view all the answers

    What is a potential consequence of relying heavily on Artificial Intelligence?

    <p>Erosion of human skills related to decision-making and problem-solving</p> Signup and view all the answers

    What is a possible approach to mitigating the negative impact of AI on job displacement?

    <p>Developing retraining programs to support workforce transition</p> Signup and view all the answers

    What is a potential risk of AI being used in social and political scenarios?

    <p>It can be used to influence public opinions and elections</p> Signup and view all the answers

    What is a key aspect of maintaining a balance between human and AI roles?

    <p>Preserving essential human skills related to decision-making and problem-solving</p> Signup and view all the answers

    What is a possible consequence of not regulating the use of AI in sensitive areas?

    <p>Increased social manipulation and influence on public opinions</p> Signup and view all the answers

    What is a potential benefit of developing policies that support workforce transition?

    <p>Improved adaptability of workers to new roles and industries</p> Signup and view all the answers

    What is a key characteristic of an approach to limit the negative impact of AI?

    <p>Maintaining a balance between human and AI roles</p> Signup and view all the answers

    What is a potential consequence of the erosion of human skills due to over-reliance on AI?

    <p>Increased unemployment rates</p> Signup and view all the answers

    Which of the following is a potential approach to limiting the impact of AI on job displacement?

    <p>Developing policies that support workforce transition through retraining programs</p> Signup and view all the answers

    What is a potential risk associated with the use of AI in social and political scenarios?

    <p>Manipulation of public opinions and elections</p> Signup and view all the answers

    What is a key challenge associated with the use of AI in sensitive areas such as media and political campaigns?

    <p>Regulating the use of AI</p> Signup and view all the answers

    What is a potential consequence of job displacement due to AI?

    <p>Increased unemployment rates</p> Signup and view all the answers

    What is a key approach to preserving essential skills in the face of AI?

    <p>Maintaining balances in human-AI roles</p> Signup and view all the answers

    What is a potential benefit of developing policies that support workforce transition through retraining programs?

    <p>Reduced impact of AI on job displacement</p> Signup and view all the answers

    What is a potential consequence of AI systems perpetuating biases present in their training data?

    <p>Unfair treatment of certain groups</p> Signup and view all the answers

    What is a key approach to limiting the impact of AI systems on privacy violations?

    <p>Implementing rigorous ethical guidelines</p> Signup and view all the answers

    What is a potential legal challenge in assigning liability when AI systems cause harm or damage?

    <p>Determining ownership of AI-generated content</p> Signup and view all the answers

    What is a key benefit of establishing clear legal frameworks for AI systems?

    <p>Providing clarity on rights and responsibilities</p> Signup and view all the answers

    What is a potential consequence of over-reliance on AI systems in customer service and caregiving?

    <p>Dehumanization in customer service and caregiving</p> Signup and view all the answers

    What is a key approach to limiting the impact of AI systems on bias and discrimination?

    <p>Implementing rigorous ethical guidelines</p> Signup and view all the answers

    What is a potential challenge in assigning liability when AI systems operate across borders?

    <p>Navigating varying international regulations</p> Signup and view all the answers

    Study Notes

    Planning and Decision-Making

    • Incomplete information and incorrect information can lead to problems in planning, including unknown preconditions, disjunctive effects, and incorrect state information.
    • The qualification problem arises when it's impossible to list all required preconditions and possible outcomes of actions.
    • Solutions to these problems include contingent or sensorless planning, conditional planning, continuous planning/replanning, and execution monitoring and replanning.

    Markov Decision Processes (MDPs)

    • MDPs are a mathematical framework used to model decision-making problems with partly random and partly controllable outcomes.
    • Components of an MDP include:
      • States (S): possible conditions or configurations of the agent.
      • Actions (A): possible actions the agent can take in each state.
      • Transition Function (P): probability of moving from one state to another given an action.
      • Reward Function (R): immediate reward or penalty received after transitioning from one state to another.
      • Start State: where the agent begins the decision process.

    Rewards and Reward Shaping

    • Rewards are scalar feedback signals given to the agent based on its actions in specific states.
    • Rewards reflect the desirability of an outcome from the agent's perspective.
    • Reward shaping modifies the reward function to make desired outcomes more apparent and immediate.
    • Challenges in reward shaping include designing an appropriate reward function and the credit assignment problem.

    Markov Property

    • If a process is Markovian, the next state depends only on the current state and the action taken in that state.
    • The Markov property simplifies analysis and computation in decision processes.
    • A practical implication of the Markov property is that the current state encapsulates all relevant information from the past needed to predict the future.

    Policy Iteration and Value Iteration

    • Policy iteration is a method for solving MDPs that involves evaluating a given policy and improving it iteratively until convergence.
    • Value iteration is an algorithm used to find the optimal policy in an MDP by updating the state values iteratively.

    Solving MDPs

    • Solving an MDP means finding an optimal policy that maximizes the cumulative reward.
    • Methods for solving MDPs include using heuristic search, value iteration, and policy iteration.

    Machine Learning

    • Machine learning can be useful in tasks that require knowledge, such as detection, classification, recognition, and prediction.
    • There are three types of feedback that can accompany inputs: supervised, unsupervised, and utility-based learning.

    Learning and Knowledge Representation

    • Explanation-based learning (EBL) extracts general rules from single examples by explaining the examples and generalizing the explanation.
    • Knowledge-based inductive learning (KBIL) finds inductive hypotheses that explain sets of observations with the help of background knowledge.
    • Relevance-based learning (RBL) uses prior knowledge to identify relevant attributes and formulate a hypothesis.

    Learning and Problem Formulation

    • Developing a machine learning system involves problem formulation, data collection, feature engineering, model selection, and training.

    • Metrics such as ROC curves and confusion matrices can be used to evaluate model performance.

    • Trust, interpretability, and explainability are important aspects of machine learning systems.### Learning Mechanisms and Types of Agents

    • Reflex Agents: do not learn, rely on pre-defined rules, limited adaptability

    • Reflex Agents with State: maintain internal state representation, adapt by updating internal state

    • Model-Based Reflex Agent: incorporate learning algorithms, adapt to changes in environment

    Learning and Adaptation

    • Adaptation Abilities: Reflex Agents - limited, Reflex Agents with State - adapt to changes, Model-Based Reflex Agent - adapt to changes
    • Learning Mechanisms: Reflex Agents - none, Reflex Agents with State - update internal state, Model-Based Reflex Agent - learning algorithms

    K-Fold Cross-Validation

    • Split data into k equal subsets
    • Perform k rounds of learning on each subset
    • Hold out 1/k of data as validation set, remaining as training set
    • Criterion for selection: minimize loss function

    Loss Function and Utility Function

    • Loss function L(x, y, yˆ) = amount of utility lost by predicting h(x) = yˆ when correct answer is f(x) = y
    • Simplified version of loss function: L(y, yˆ)
    • Learning agent maximizes expected utility by choosing hypothesis that minimizes expected loss

    Parametric and Nonparametric Models

    • Parametric Models: summarize data with a set of parameters of fixed size (independent of number of training examples)
    • Nonparametric Models: cannot be characterized by a bounded set of parameters
    • Example of Nonparametric Model: Table lookup, take all training examples and put in lookup table

    Explanation-Based Learning (EBL)

    • Cumulative learning process that uses background knowledge and its extension over time
    • Extends knowledge by extracting general rules from individual observations
    • Creates general rules that cover an entire class of cases

    Machine Learning

    • Detection: discovering implicitly present interference from the outside world
    • Classification: grouping items into categories based on certain discriminating characteristics
    • Recognition: establishing the class of an item based on common attributes
    • Identification: unambiguously recognizing an item based on unique attributes
    • Prediction: predicting the appearance of a particular object, class, or pattern

    Three Types of Feedback

    • Supervised Learning: agent observes input-output pairs, learns a function that maps from input to output
    • Unsupervised Learning: agent processes data input, learns patterns in input without explicit feedback
    • Utility-based Learning: agent learns from a series of reinforcements (rewards and punishments)

    Developing Machine Learning Systems

    • Problem formulation: define problem, input, output, and loss function
    • Data collection, assessment, and management: when data are limited, data augmentation can help
    • Feature engineering and exploratory data analysis (EDA)
    • Model selection and training
    • Receiver operating characteristic (ROC) curve
    • Trust, interpretability, and explainability
    • Bias and Discrimination: AI systems can perpetuate biases present in training data
    • Privacy Violations: AI technologies can intrude on individuals’ privacy
    • Lack of Accountability: unclear who is responsible for actions of AI systems
    • Dehumanization: over-reliance on AI can lead to dehumanization in various sectors
    • Legal Problems: intellectual property issues, liability for harm, compliance with international laws
    • Social Problems: job displacement, erosion of human skills, social manipulation

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    Test your understanding of Markov Decision Processes, a mathematical framework for modelling decision-making problems with random and controllable outcomes. Learn about MDP components and Markov Decision Policies.

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