CSE537: Artificial Intelligence

CSE537: Artificial Intelligence

Quiz Course Website : http://www3.cs.stonybrook.edu/~ram/cse537

Chapter 1.1 : What is AI?

Chapter 1.2 : The Foundation of Artificial Intelligence

Chapter 1.3 : The History of Artificial Intelligence

Chapter 1.4 : The State of the Art

Chapter 1.5 : Summary

Chapter 2 : Intelligent Agents

Chapter 2.1 : Agents & Environments

Chapter 2.2 : Good Behavior: The Concept of Rationality

Chapter 2.3 : The Nature of Environments

Chapter 2.4 : The Structure of Agents

Chapter 2.5 : Summary

Chapter 3 : Solving Problems by Search

Chapter 3.1 : Problem Solving Agents

Chapter 3.2 : Example Problems

Chapter 3.3 : Searching for Solutions

Chapter 3.4 : Uninformed Search Strategies

Chapter 3.5 : Informed (Heuristic) Search Strategies

Chapter 3.6 : Heuristic Functions

Chapter 3.7 : Summary, Bibliography and Historical Notes, Exercises

Chapter 4 : Beyond Classical Search

Chapter 4.1 : Local Search Algorithms & Optimization Problems

Chapter 4.2 : Local Search in Continuous Spaces

Chapter 4.3 : Searching with Nondeterministic Actions

Chapter 4.4 : Searching with Partial Observations

Chapter 4.5 : Online Search Agents and Unknown Environments

Chapter 4.6 : Summary

Bibliography and Historical Notes

Chapter 5.1 : Games

Chapter 5.2 : Optimal Decisions in Games

Chapter 5.3 : Alpha-Beta Pruning

Chapter 5.4 : Imperfect Real-Time Decisions

Chapter 5.5 : Stochastic Games

Chapter 5.6 : Partially Observable Games

Chapter 5.7 : State-of-the-Art Game Programs

Chapter 5.8 : Alternative Approaches

Chapter 5.9 : Summary

Bibliographical and Historical Notes

Chapter 6 : Constraint Satisfaction Problems

Chapter 6.1 : Defining Constraint Satisfaction Problems

Chapter 6.2 : Constraint Propagation: Inference in CSPs

Chapter 6.3 : Backtracking Search for CSPs

Chapter 6.4 : Local Search for CSPs

Chapter 6.5 :The Structure of Problems

Chapter 6.6 : Summary, Bibliographic and Historical Notes, Exercises

Chapter 7.1 : Knowledge-Based Agents

Chapter 7.3 : Logic

Chapter 7.4 : Propositional Logic: A Very Simple Logic

Chapter 7.5 : Propositional Theorem Proving

Chapter 9.4 : Backward Chaining

Chapter 9.5 : Resolution

Chapter 13 : Quantifying Uncertainty

Chapter 13.1 : Acting under Uncertainty

Chapter 13.2 : Basic Probability Notation

Chapter 13.3 : Inference Using Full Joint Distributions

Chapter 13.4 : Independence

Chapter 13.5 : Bayes' Rule and Its Use

Chapter 13.6 : The Wumpus World Revisited

Chapter 13.7 : Summary, Bibliographic and Historical Notes, Exercises

Chapter 14 : Probabilistic Reasoning

Chapter 14.1 : Representing Knowledge in an Uncertain Domain

Chapter 14.2 : The Semantics of Bayesian Networks

Chapter 14.3 : Efficient Representation of Conditional Distributions

Chapter 14.4 : Exact Inference in Bayesian Networks

Chapter 14.5 : Approximate Inference in Bayesian Networks

Chapter 14.6 : Relational and First-Order Probability Models

Chapter 14.7 : Other Approaches to Uncertain Reasoning

Chapter 14.8 : Summary, Bibliographical and Historical Notes, Exercises

Chapter 15 : Probabilistic Reasoning over Time

Chapter 15.1 : Time and Uncertainty

Chapter 15.2 : Inference in Temporal Models

Chapter 15.3 : Hidden Markov Models

Chapter 15.4 : Kalman Filters

Chapter 15.5 : Dynamic Bayesian Networks

Chapter 15.6 : Keeping Track of Many Objects

Chapter 15.7 : Summary, Bibliographical and Historical Notes, Exercises

Chapter 18 : Learning from Examples

Chapter 18.1 : Forms of Learning

Chapter 18.2 : Supervised Learning

Chapter 18.3 : Learning Decision Trees

Chapter 18.4 : Evaluating and Choosing the Best Hypothesis

Chapter 18.5 : The Theory of Learning

Chapter 18.6 : Regression and Classification with Linear Models

Chapter 18.7 : Artificial Neural Networks

Chapter 18.8 : Nonparametric Models

Chapter 18.9 : Support Vector Machines

Chapter 18.10 : Ensemble Learning

Chapter 18.11 : Practical Machine Learning

Chapter 18.12 : Summary, Bibliographical and Historical Notes, Exercises

Chapter 20.2 : Learning with Complete Data

Chapter 20.3 : Learning with Hidden Variables: The EM Algorithm

Chapter 22.1 : Language Models

Chapter 22.2 : Text Classification

Chapter 22.3 : Information Retrieval

Chapter 22.4 : Information Extraction