Course Content

Module 1: Introduction

What Is AI?

The Foundations of Artificial Intelligence

TheHistoryofArtificialIntelligence

TheStateoftheArt

Risks and Benefits of AI

 

 

Module 2:

Intelligent Agents

AgentsandEnvironments

Good Behavior:TheConceptofRationality

The Nature of Environments

The Structure of Agents

 

Module 4: Solving Problems by Searching

Problem-Solving Agents

Example Problems

Search Algorithms

UninformedSearchStrategies

Informed(Heuristic)SearchStrategies

HeuristicFunctions

 

 

Module 4

Search in Complex Environments

LocalSearchandOptimizationProblems

LocalSearchinContinuousSpaces

SearchwithNondeterministicActions

Searching Partially Observable Environments

Online Search Agents and Unknown Environments

 

 

Module 5

Adversarial Search and Games

GameTheory

OptimalDecisionsinGames

HeuristicAlpha–BetaTreeSearch

MonteCarloTreeSearch

StochasticGames

PartiallyObservableGames

LimitationsofGameSearchAlgorithms

 

 

Module 6

Constraint Satisfaction Problems

DefiningConstraintSatisfactionProblems

ConstraintPropagation:InferenceinCSPs

BacktrackingSearchforCSPs

LocalSearchforCSPs

TheStructureofProblems

 

 

Module 7

Logical Agents 208

Knowledge-BasedAgents

TheWumpusWorld

Logic

PropositionalLogic:AVerySimpleLogic

PropositionalTheoremProving

EffectivePropositionalModelChecking

AgentsBasedonPropositionalLogic

 

Module 8

First-Order Logic

RepresentationRevisited

SyntaxandSemanticsofFirst-OrderLogic

UsingFirst-OrderLogic

KnowledgeEngineeringinFirst-OrderLogic

 

 

Module 9

Inference in First-Order Logic

Propositionalvs.First-OrderInference

UnificationandFirst-OrderInference

ForwardChaining

BackwardChaining

Resolution

 

Module 10

Knowledge Representation

OntologicalEngineering

CategoriesandObjects

Events

MentalObjectsandModalLogic

ReasoningSystemsforCategories

ReasoningwithDefaultInformation

 

 

Module 11

Automated Planning

DefinitionofClassicalPlanning

AlgorithmsforClassicalPlanning

HeuristicsforPlanning

HierarchicalPlanning

Planning and Acting in Nondeterministic Domains

Time,Schedules,andResources

AnalysisofPlanningApproaches

 

 

Module 12

Quantifying Uncertainty

ActingunderUncertainty

BasicProbabilityNotation

InferenceUsingFullJointDistributions

Independence

Bayes’RuleandItsUse

NaiveBayesModels

TheWumpusWorldRevisited

 

 

Module 13

Probabilistic Reasoning

RepresentingKnowledgeinanUncertainDomain

TheSemanticsofBayesianNetworks

ExactInferenceinBayesianNetworks

ApproximateInferenceforBayesianNetworks

CausalNetworks

 

Module 14

Probabilistic Reasoning over Time

TimeandUncertainty

InferenceinTemporalModel

HiddenMarkovModels

KalmanFilters

DynamicBayesianNetworks

 

Module 15

Probabilistic Programming

RelationalProbabilityModels

UniverseProbabilityModels

KeepingTrackofaComplexWorld

ProgramsasProbabilityModels

 

Module 16

Making Simple Decisions

Combining Beliefs and Desires under Uncertainty

TheBasisofUtilityTheory

UtilityFunctions

MultiattributeUtilityFunctions

DecisionNetworks

TheValueofInformation

UnknownPreferences

 

Module 17

Making Complex Decisions

SequentialDecisionProblems

AlgorithmsforMDPs

BanditProblems

PartiallyObservableMDPs

AlgorithmsforSolvingPOMDPs

 

Module 18

Multiagent Decision Making 599

PropertiesofMultiagentEnvironments

Non-CooperativeGameTheory

CooperativeGameTheory

MakingCollectiveDecisions

 

Module 19

Learning from Examples

FormsofLearning

SupervisedLearning

LearningDecisionTrees

ModelSelectionandOptimization

TheTheoryofLearning

LinearRegressionandClassification

NonparametricModels

EnsembleLearning

DevelopingMachineLearningSystems

 

Module 20

Learning Probabilistic Models

StatisticalLearning

LearningwithCompleteData

LearningwithHiddenVariables:TheEMAlgorithm

 

Module 21

Deep Learning

SimpleFeedforwardNetworks

ComputationGraphsforDeepLearning

ConvolutionalNetworks

LearningAlgorithms

Generalization

RecurrentNeuralNetworks

UnsupervisedLearningandTransferLearning

Applicationsy

 

Module 22

Reinforcement Learning

LearningfromRewards

PassiveReinforcementLearning

ActiveReinforcementLearning

GeneralizationinReinforcementLearning

PolicySearch

Apprenticeship and Inverse Reinforcement Learning

ApplicationsofReinforcementLearning

 

 

Module 23

Natural Language Processing

LanguageModels

Grammar

Parsing

AugmentedGrammars

ComplicationsofRealNaturalLanguage

NaturalLanguageTasks

 

 

Module 24

Deep Learning for Natural Language Processing

WordEmbeddings

RecurrentNeuralNetworksforNLP

Sequence-to-SequenceModels

TheTransformerArchitecture

PretrainingandTransferLearning

Stateoftheart

 

Module 25

Computer Vision

Introduction

ImageFormation

SimpleImageFeatures

ClassifyingImages

DetectingObjects

The3DWorld

UsingComputerVision

 

Module 26

Robotics

Robots

RobotHardware

Whatkindofproblemisroboticssolving?

RoboticPerception

PlanningandControl

PlanningUncertainMovements

ReinforcementLearninginRobotics

HumansandRobots

AlternativeRoboticFrameworks

ApplicationDomains

 

Module 27

Philosophy, Ethics, and Safety of AI

TheLimitsofAI

CanMachinesReallyThink?

TheEthicsofAI

 

Module 28

The Future of AI

AIComponents

AIArchitectures

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