Artificial intelligence basics / Neeru Gupta, Ramita Mangla
Material type:
TextPublication details: Estados unidos : Mercury learning, 2020Description: 203 páginas, Ilustraciones; 23 cmContent type: - Texto
- Sin mediación
- Volumen
- 9781683925163
- 006.31 G977 2020 23
| Item type | Current library | Collection | Call number | Materials specified | Copy number | Status | Notes | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|---|---|
Libro de Reserva
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Biblioteca William Corredor Gómez. Sede Cosmos (Barranquilla) | Reserva | 006.31 G977 2020 (Browse shelf(Opens below)) | Ingeniería de Sistemas / Barranquilla | Ej. 1 | Available | Colección 1, Isla 1, Lado B, Módulo 3 | 301257619 |
Browsing Biblioteca William Corredor Gómez. Sede Cosmos (Barranquilla) shelves, Collection: Reserva Close shelf browser (Hides shelf browser)
| 006.3 R961 2004 Inteligencia artificial : | 006.3 S669 2006 Redes neuronales : | 006.31 C195 2020 Artificial intelligence machine learning and deep learning / | 006.31 G977 2020 Artificial intelligence basics / | 006.31 I616 2021 Introducción al machine learning con matlab / | 006.31 P438 2015 Minería de datos a través de ejemplos / | 006.33 G977 2020 Artificial intelligence and expert systems / |
1. ARTIFICIAL INTELLIGENCE.
1.1 Computerized reasoning.
1.2 Turing test.
1.3 What is intelligence?.
1.4 Artificial intelligence.
1.5 Goals of artificial intelligence.
1.6 History of artificial intelligence.
1.6 History of artificial intelligence.
1.7 Advantages of artificial intelligence.
1.8 Application areas of artificial intelligence.
2. PROBLEM REPRESENTATION.
2.1 Introduction.
2.2 Problem characteristics.
2.3 Problem representation in AI.
2.4 Production system.
2.5 conflict resolution.
3. THE SEARCH PROCESS.
3.1 Search process.
3.2 Strategies for search.
3.3 Search techniques.
4.GAME PLAYING.
4.1 Game playing.
4.2 Game tree.
4.3 Components of game playing program.
4.4 Game playing strategies.
4.5 Problems in computer game playing programs.
5.KNOWLEDGE REPRESENTATION.
5.1Introduction.
5.2 Definition of knowledge.
5.3 Importance of knowledge.
5.4 Knowledge-based systems.
5.5 Differences between knowledge-based system and database systems.
5.6 Knowledge representation scheme.
6. EXPERT SYSTEMS.
6.1 Introduction.
6.2 Definition of an expert system.
6.3 Characteristics of an expert system.
6.4 Architecture of expert systems.
6.5 Expert system life cycle.
6.6 Knowledge engineering process.
6.7 knowledge acquisition.
6.8 Difficulties in knowledge acquisition.
6.9 Knowledge acquisition strategies.
6.10 Advantages of expert systems.
6.11 Limitations of expert systems.
6.12 Examples of expert systems.
7. LEARNING.
7.1 Learning.
7.2 general model for machine learning systems.
7.3 Characteristics of machine learning.
7.4 Types of learning.
7.5 Advantages of machine learning.
7.6 Disadvantages of machine learning.
8. PROLOG.
8.1 Preliminaries of prolog.
8.2 Milestones in prolog language development.
8.3 What is a horn clause?.
8.4 Robinson´s resolution rule.
8.5 Parts of a prolog program.
8.6 Queries to a database.
8.7 How does prolog solve a query?.
8.8 Compound queries.
8.9 The_variable.
8.10 Recursion in prolog.
8.12 Head and tail of a list.
8.13 Print all the members of the list.
8.14 Print the list in reverse order.
8.15 Appending a list.
8.16 Find whether the given item is a member of the list.
8.17 Finding the length of the list.
8.18 Controlling execution in prolog.
8.19 About turbo prolog.
9. PYTHON.
9.1 Languages used for building AI.
9.2 Why do people choose python?.
9.3 Build AI using python.
9.4 Running python.
9.5 Pitfalls.
9.6 Features of python.
9.7 Useful libraries.
9.8 Utilities.
9.9 Testing code.
10. ARTIFICIAL INTELLIGENCE MACHINES AND ROBOTICS.
10.0 Introduction.
10.1 History serving, emulating, enhancing, and replacing man.
10.2 Technical issues.
10.3 Applications: robotics in the twenty-first century.
10.4 Summary.
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