Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
Product Description
Matrix Algebra: Theory, Computations and Applications in Statistics (Springer Texts in Statistics)
This much-needed work presents, among other things, the relevant aspects of the theory of matrix algebra for applications in statistics. Written in an informal style, it addresses computational issues and places more emphasis on applications than existing texts.
Technical Specifications
You might also like
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Applied Predictive Modeling
The Hundred-Page Machine Learning Book
Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
Machine Learning: An Applied Mathematics Introduction