Official course description, subject to change:
Preliminary info last published 7/0520
Linear Algebra and Probability
Course info
Language:
English
ECTS points:
7.5
Course code:
KSLIALP1KU
Participants max:
70
Offered to guest students:
yes
Offered to exchange students:

Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
MSc in Computer Science
Staff
Course semester
Semester
Forår 2021
Start
25 January 2021
End
28 May 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7trinsskala
Exam Language
GB
Abstract
This is a course in mathematics covering linear algebra and basic probability theory. This course is the first course of the Algorithms and Machine Learning specialisations.Description
These topics covered by this course are important in various branches of computer science, in particular in algorithms and machine learning.
Successful students will acquire skills in Linear Algebra and Probability Theory.
The topics covered in the linear algebra part of the
course include systems of linear equations, matrices, determinants, vector
spaces, bases, dimension, and eigenvectors. The topics covered in the
probability theory part include conditional probability, discrete and
continuous random variables, as well as the limit theorems. A number of
applications of the material will be covered in the course, including least
squares analysis and Google’s PageRank algorithm.
Formal prerequisites
The course assumes that the students have taken the course Foundation of Computing: Discrete Mathematics from the BSc in Software Development or similar.Intended learning outcomes
After the course, the student should be able to:
 Solve systems of linear equations
 Define the basic concepts of linear algebra and probability, e.g., eigenvalues for a matrix or variance of a discrete random variable
 Compute the essential constructions of linear algebra, such as the inverse of a given matrix or the eigenvectors of a given matrix. Compute probabilities, expected values, variances and other concepts from probability theory.
 Apply the tools of linear algebra and probability to solve small mathematical problems.
 Model simple probabilistic problems using the distributions covered in the course.
Ordinary exam
Exam type:C: Submission of written work, External (7point scale)
Exam variation:
C22: Submission of written work – Take home