Buch, Englisch, 459 Seiten, Paperback, Format (B × H): 168 mm x 240 mm, Gewicht: 785 g
Buch, Englisch, 459 Seiten, Paperback, Format (B × H): 168 mm x 240 mm, Gewicht: 785 g
ISBN: 978-981-1519-69-7
Verlag: Springer Nature Singapore
The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.
Zielgruppe
Upper undergraduate
Fachgebiete
Weitere Infos & Material
1 Introduction.- 2 Model Selection and Evaluation.- 3 Linear Models.- 4 Decision Trees.- 5 Neural Networks.- 6 Support Vector Machine.- 7 Bayes Classifiers.- 8 Ensemble Learning.- 9 Clustering.- 10 Dimensionality Reduction and Metric Learning.- 11 Feature Selection and Sparse Learning.- 12 Computational Learning Theory.- 13 Semi-Supervised Learning.- 14 Probabilistic Graphical Models.- 15 Rule Learning.- 16 Reinforcement Learning.