Mathematics for machine learning
"The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or...
Saved in:
| Main Author: | Deisenroth, Marc Peter |
|---|---|
| Other Authors: | Aldo Faisal, A |
| Format: | Book |
| Language: | Undetermined |
| Published: |
Cambridge ;New York, NY
Cambridge University Press
2020
|
| Subjects: | |
| Online Access: | http://lrc.tdmu.edu.vn/opac/search/detail.asp?aID=2&ID=41689 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institutions: | Trung tâm Học liệu Trường Đại học Thủ Dầu Một |
|---|
Similar Items
-
Data Science and Machine Learning: Mathematical and Statistical Methods
by: Dirk, P. Kroese, et al.
Published: (2025) -
Introduction to machine learning /
by: Nilsson, Nils J.
Published: (1996) -
Personalized Machine Learning
by: Julian, McAuley
Published: (2026) -
Introduction to machine learning : An early draft of a proposed textbook /
by: Nilsson, Nils J.
Published: (1996) -
Correlation-based feature selection for machine learning /
by: Hall, Mark A.
Published: (1999)