AI-APPROACH
Artificial intelligence: A modern approach
Der Kurs „Künstliche Intelligenz: Ein moderner Ansatz“ beleuchtet die gesamte Bandbreite und Tiefe der KI-Technologie. Der umfassende Kurs hilft beim Verständnis von Themen wie maschinelles Lernen, Deep Learning, Transfer Learning, Multiagentensysteme, Robotik, Verarbeitung natürlicher Sprache, Kausalität, probabilistische Programmierung, Datenschutz, Fairness und sichere KI. Der Kurs „Künstliche Intelligenz“ vermittelt Studierenden ein grundlegendes Verständnis der neuesten Entwicklungen im Bereich KI, ohne dabei Komplexität und Tiefe zu vernachlässigen. Er zeigt ihnen außerdem, wie die verschiedenen Teilbereiche der KI zusammenwirken, um konkrete, nützliche Programme zu entwickeln. Tutorial)
- 30 Interaktive Lektionen Und 187 topics mapped to the official exam objectives
Intermediate Selbstgesteuert · 1 Jahr Zugang 4.8/5 (229 Rezension)
30Interaktive Lektionen
187Topics
146Karteikarten
146Glossar der Begriffe
01 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
30 Interaktive Lektionen · 187 topics01 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
Keep going
You might also like
Bereiten Sie sich vor auf Artificial intelligence: A modern approach
Einmalige Zahlung. Voller Zugang für 1 Jahr. Starten Sie mit einer kostenlosen Testversion, wenn Sie sich zunächst umsehen möchten.