AI-APPROACH

Artificial intelligence: A modern approach

Artificial intelligence: A modern approach course explores the full breadth and depth of artificial intelligence technology. The comprehensive course helps in understanding the topics such as machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI. The artificial intelligence course provides students with a basic understanding of the frontiers of AI without compromising complexity and depth and also shows students how the various subfields of AI fit together to build actual, useful programs.

30 Interactive Lessons and 187 topics mapped to the official exam objectives

Intermediate Self-paced · 1 year access 4.8/5 (229 Reviews)

30Interactive Lessons

187Topics

146Flashcards

146Glossary of terms

01 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

30 Interactive Lessons · 187 topics

01 Introduction 5 topics

What Is AI?
The Foundations of Artificial Intelligence
The History of Artificial Intelligence
The State of the Art
Summary, Bibliographical and Historical Notes, Exercises

02 Intelligent Agents 5 topics

Agents and Environments
Good Behavior: The Concept of Rationality
The Nature of Environments
The Structure of Agents
Summary, Bibliographical and Historical Notes, Exercises

03 Solving Problems by Searching 8 topics

Problem-Solving Agents
Example Problems
Searching for Solutions
Uninformed Search Strategies
Informed (Heuristic) Search Strategies
Heuristic Functions
Finding Relevant Code
Summary, Bibliographical and Historical Notes, Exercises

04 Beyond Classical Search 6 topics

Local Search Algorithms and Optimization Problems
Local Search in Continuous Spaces
Searching with Nondeterministic Actions
Searching with Partial Observations
Online Search Agents and Unknown Environments
Summary, Bibliographical and Historical Notes, Exercises

05 Adversarial Search 9 topics

Games
Optimal Decisions in Games
Alpha–Beta Pruning
Imperfect Real-Time Decisions
Stochastic Games
Partially Observable Games
State-of-the-Art Game Programs
Alternative Approaches
Summary, Bibliographical and Historical Notes, Exercises

06 Constraint Satisfaction Problems 6 topics

Defining Constraint Satisfaction Problems
Constraint Propagation: Inference in CSPs
Backtracking Search for CSPs
Local Search for CSPs
The Structure of Problems
Summary, Bibliographical and Historical Notes, Exercises

07 Logical Agents 8 topics

Knowledge-Based Agents
The Wumpus World
Logic
Propositional Logic: A Very Simple Logic
Propositional Theorem Proving
Effective Propositional Model Checking
Agents Based on Propositional Logic
Summary, Bibliographical and Historical Notes, Exercises

08 First-Order Logic 5 topics

Representation Revisited
Syntax and Semantics of First-Order Logic
Using First-Order Logic
Knowledge Engineering in First-Order Logic
Summary, Bibliographical and Historical Notes, Exercises

09 Inference in First-Order Logic 6 topics

Propositional vs. First-Order Inference
Unification and Lifting
Forward Chaining
Backward Chaining
Resolution
Summary, Bibliographical and Historical Notes, Exercises

10 Classical Planning (Supplemental) 6 topics

Definition of Classical Planning
Algorithms for Planning as State-Space Search
Planning Graphs
Other Classical Planning Approaches
Analysis of Planning Approaches
Summary, Bibliographical and Historical Notes, Exercises

11 Planning and Acting in the Real World 5 topics

Time, Schedules, and Resources
Hierarchical Planning
Planning and Acting in Nondeterministic Domains
Multiagent Planning
Summary, Bibliographical and Historical Notes, and Exercise

12 Knowledge Representation 8 topics

Ontological Engineering
Categories and Objects
Events
Mental Events and Mental Objects
Reasoning Systems for Categories
Reasoning with Default Information
The Internet Shopping World
Summary, Bibliographical and Historical Notes, Exercises

13 Quantifying Uncertainty 7 topics

Acting under Uncertainty
Basic Probability Notation
Inference Using Full Joint Distributions
Independence
Bayes' Rule and Its Use
The Wumpus World Revisited
Summary, Bibliographical and Historical Notes, Exercises

14 Probabilistic Reasoning 8 topics

Representing Knowledge in an Uncertain Domain
The Semantics of Bayesian Networks
Efficient Representation of Conditional Distributions
Exact Inference in Bayesian Networks
Approximate Inference in Bayesian Networks
Relational and First-Order Probability Models
Other Approaches to Uncertain Reasoning
Summary, Bibliographical and Historical Notes, Exercises

15 Probabilistic Reasoning over Time (Supplemental) 7 topics

Time and Uncertainty
Inference in Temporal Models
Hidden Markov Models
Kalman Filters
Dynamic Bayesian Networks
Keeping Track of Many Objects
Summary, Bibliographical and Historical Notes, Exercises

16 Making Simple Decisions (Supplemental) 8 topics

Combining Beliefs and Desires under Uncertainty
The Basis of Utility Theory
Utility Functions
Multiattribute Utility Functions
Decision Networks
The Value of Information
Decision-Theoretic Expert Systems
Summary, Bibliographical and Historical Notes, Exercises

17 Making Complex Decisions (Supplemental) 7 topics

Sequential Decision Problems
Value Iteration
Policy Iteration
Partially Observable MDPs
Decisions with Multiple Agents: Game Theory
Mechanism Design
Summary, Bibliographical and Historical Notes, Exercises

18 Learning from Examples 12 topics

Forms of Learning
Supervised Learning
Learning Decision Trees
Evaluating and Choosing the Best Hypothesis
The Theory of Learning
Regression and Classification with Linear Models
Artificial Neural Networks
Nonparametric Models
Support Vector Machines
Ensemble Learning
Practical Machine Learning
Summary, Bibliographical and Historical Notes, Exercises

19 Knowledge in Learning 8 topics

A Logical Formulation of Learning
Knowledge in Learning
Explanation-Based Learning
Learning Using Relevance Information
Inductive Logic Programming
Feature Space Engineering
Data Preparation and Preprocessing
Summary, Bibliographical and Historical Notes, Exercises

20 Learning Probabilistic Models 4 topics

Statistical Learning
Learning with Complete Data
Learning with Hidden Variables: The EM Algorithm
Summary, Bibliographical and Historical Notes, Exercises

21 Reinforcement Learning 7 topics

Introduction
Passive Reinforcement Learning
Active Reinforcement Learning
Generalization in Reinforcement Learning
Policy Search
Applications of Reinforcement Learning
Summary, Bibliographical and Historical Notes, Exercises

22 Natural Language Processing (Supplemental) 5 topics

Language Models
Text Classification
Information Retrieval
Information Extraction
Summary, Bibliographical and Historical Notes, Exercises

23 Natural Language for Communication (Supplemental) 6 topics

Phrase Structure Grammars
Syntactic Analysis (Parsing)
Augmented Grammars and Semantic Interpretation
Machine Translation
Speech Recognition
Summary, Bibliographical and Historical Notes, Exercises

24 Perception (Supplemental) 7 topics

Image Formation
Early Image-Processing Operations
Object Recognition by Appearance
Reconstructing the 3D World
Object Recognition from Structural Information
Using Vision
Summary, Bibliographical and Historical Notes, Exercises

25 Robotics 9 topics

Introduction
Robot Hardware
Robotic Perception
Planning to Move
Planning Uncertain Movements
Moving
Robotic Software Architectures
Application Domains
Summary, Bibliographical and Historical Notes, Exercises

26 Focus: Robotics and Feature Engineering 2 topics

Coppelia Robotics
Robotics: Feature Engineering

27 Philosophical Foundations 4 topics

Weak AI: Can Machines Act Intelligently?
Strong AI: Can Machines Really Think?
The Ethics and Risks of Developing Artificial Intelligence
Summary, Bibliographical and Historical Notes, Exercises

28 AI: The Present and Future 4 topics

Agent Components
Agent Architectures
Are We Going in the Right Direction?
What If AI Does Succeed?

29 Appendix A: Mathematical background 3 topics

A.1 Complexity Analysis and O() Notation
A.2. Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions

30 Appendix B: Notes on Languages and Algorithms 2 topics

B.1 Defining Languages with Backus–Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode

Prepare for Artificial intelligence: A modern approach

One-time payment. Full access for 1 year. Start with a free trial if you want to look around first.

1 year of full access
Certificate of completion

Buy Now — $339.99 Try Free

No credit card required

Artificial intelligence: A modern approach

See exactly what you will learn and practice

Lessons

You might also like

Prepare for Artificial intelligence: A modern approach