Official course description:
Full info last published 15/11-19

### Linear Algebra and Probability

##### Course info
Language:
English
ECTS points:
7.5
Course code:
KSLIALP1KU
Participants max:
70
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
##### Programme
Level:
MSc. Master
Programme:
Master of Science in Computer Science
##### Staff
Course manager
Associate Professor
Semester
Forår 2020
Start
27 January 2020
End
31 August 2020
##### 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.
##### Learning activities

Lectures and exercises.
At the exercise sessions the students will solve and present solutions to mathematical problems. The mandatory assignments are of the form of mathematical problems, but can also involve some programming. The solutions must be submitted in written form.

##### Mandatory activities
There are 6 mandatory assignments, out of which 5 must be approved for the student to qualify for the exam. The deadlines are evenly distributed over the semester (approximately one every 2 weeks), exact dates will be posted on learnit the first week of the semester. If a mandatory assignment is not approved the first time, the student will be allowed to resubmit approximately one week after the first deadline.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

##### Course literature

Ron Larson: Elementary Linear Algebra, 8th ed, metric version

Dimitri Bertsekas and John Tsitsiklis: Introduction to Probability, 2nd ed.

##### Student Activity Budget
Estimated distribution of learning activities for the typical student
• Preparation for lectures and exercises: 40%
• Lectures: 20%
• Exercises: 20%
• Assignments: 20%
##### Ordinary exam
Exam type:
A: Written exam on premises, external (7-trinsskala)
Exam variation:
A33: Written exam on premises on paper with restrictions
Exam duration:
4 hours
Aids allowed for the exam:
Written and printed books and notes
E-books and/or other electronic devices
• E-books on labtops and tablets

Exam type:

Exam variation: