Torrent details for "Denis D. Multivariate Statistics and Machine Learning...Using R …" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
37.0 MB
Info Hash:
B428EC03B2834018F6C9D81C339EBEAC8B3CC2E8
Added By:
Added:
March 2, 2026, 4:37 p.m.
Stats:
|
(Last updated: May 29, 2026, 10:45 p.m.)
| File | Size |
|---|---|
| Denis D. Multivariate Statistics and Machine Learning...Using R and Python 2026.pdf | 15.3 MB |
| Denis D. Applied Univariate, Bivariate, and Multivariate Statistics Using Python...2021.pdf | 21.7 MB |
Name
DL
Uploader
Size
S/L
Added
-
827.3 MB
[36
/
19]
2025-06-25
| Uploaded by CurlyQ2 | Size 827.3 MB | Health [ 36 /19 ] | Added 2025-06-25 |
NOTE
SOURCE: Denis D. Multivariate Statistics and Machine Learning...Using R and Python 2026
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format
Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.
The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued.
With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.
Preliminaries and Foundations
Introduction, Motivation, Pedagogy, and Ideas About Learning
First Principles and Philosophical Foundations
Mathematical and Statistical Foundations
R and Python Software
Models and Methods
Univariate and Multivariate Analysis of Variance Models
Simple Linear and Multiple Regression Models (and Extensions)
Regularization Methods in Regression
Ridge, Lasso, and Elastic Net
Nonlinear and Nonparametric Regression
Generalized Linear and Additive Models
Logistic and Related Models
Support Vector Machines
Decision Trees, Bagging, Random Forests and Committee Machines
Principal Components Analysis, Blind Source Separation, and Manifold Learning
Exploratory Factor Analysis
Confirmatory Factor Analysis, Path Analysis, and Structural Equation Modeling
Cluster Analysis and Data Segmentation
Artificial Neural Networks and Deep Learning
×