Prêtable
| Titre : | Machine Learning and Artificial Intelligence |
| Auteurs : | Ameet Joshi, Auteur |
| Type de document : | texte imprimé |
| Mention d'édition : | 2nd ed. |
| Année de publication : | 2023 |
| ISBN/ISSN/EAN : | 978-3-031-12281-1 |
| Format : | 268p. / ill.en coul. / 24cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Index. décimale : | 621.382 (Technologie des communications (Technologie des données numériques, télécommunications, systèmes de communications, radiocommunication) ) |
| Catégories : | |
| Mots-clés: | Communications Engineering, Networks ; Machine Learning ; Artificial Intelligence ; Computational Intelligence |
| Résumé : | The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs. |
| Note de contenu : |
Sammary:
Partie 1: Introduction Chapitre1: Introduction to AI and ML Chapitre2: Essential Concepts in Artificial Intelligence and Machine Learning. Chapitre3: Data Understanding, Chapitre4: Representation, and Visualization Chapitre5: Implementing machine learning algorithms Partie 2: Machine Learning Chapitre6: Linear Methods Chapitre7: Perceptron and Neural Chapitre8: Networks. Chapitre9: Decision Trees Chapitre10: Support Vector Machines Chapitre11: Dynamic Programming and Reinforcement Learning. Chapitre12: Evolutionary Algorithms Chapitre13: Time Series Models Chapitre14: Deep Learning Chapitre15: Unsupervised Learning Partie 3:Building end to end pipelines Chapitre16: Designing and Tuning.- Model Chapitre17: Pipelines. Chapitre18: Performance Measurement Partie 4: Artificial intelligence Chapitre19: Classification Chapitre20: Regression Chapitre21: Ranking Chapitre22: Recommendations Systems |
Exemplaires (2)
| Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|
| F8/12677 | Livre | Bibliothèque de la Faculté de Technologie | Salle des livres | Sorti jusqu'au 27/10/2024 |
| F8/12678 | Livre | Bibliothèque de la Faculté de Technologie | Salle des livres | Disponible |
