Master in Statistics for Data Science (Plan: 386 - Estudio: 345)
EPI
Coordinating teacher: TERAN VERGARA, FERNANDO DE
Department assigned to the subject: Mathematics Department
Type: Compulsory
ECTS Credits: 3.0 ECTS
Course: 1º
Semester: 1º
Objectives
While there are many applied mathematics techniques and concepts that are useful (and used) in Data Science, this course focus on the basics of those based on linear algebra and calculus, as they underlie many of the most importants applications and algorithms: Matrix algebra, Matrix decompositions.
Description of contents: programme
1. Linear Systems
2. Vectors
3. Matrices
4. Diagonalization
5. Orthogonality
6. Symmetric Matrices
Learning activities and methodology
Theoretical classes (lectures)
Practical problems that students must solve individually as homework
Tutorials
Assessment System
% end-of-term-examination 100
% of continuous assessment (assigments, laboratory, practicals...) 0
Basic Bibliography
David C. Lay, Steven R. Lay, Judi J. McDonald. Linear Algebra and Its Applications. Pearson; 5 edition. 2016
Additional Bibliography
Gilbert Strang. LINEAR ALGEBRA and learning from Data. Wellesley Cambridge Press. 2019
W. Keith Nicholson. Linear Algebra with Applications. McGraw-Hill, 6th edition. 2009
Electronic Resources *
Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong · Mathematics for Machine Learning : https://mml-book.github.io/
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN
The course syllabus may change due academic events or other reasons.
Electronic Resources *