MATH280 Applied Linear Algebra

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Showing course contents for the educational year 2022 - 2023 .

Course responsible: Geir Bogfjellmo
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: EN
(NO=norsk, EN=Engelsk)
Teaching exam periods:
This course starts in Spring parallel. This course has teaching/evaluation in the spring parallel, .
Course frequency: Annually.
First time: Study year 2009-2010
Course contents:

Theory

Basic concepts and methods in applied linear algebra, including:

• Numerical aspects associated with solving Linear Equations
• Vector Spaces and Linear Transformations
• Diagonalization and change of Change of Coordinate Basis
• Inner Products, Length, Orthogonality and Inner Product Spaces
• Orthogonal Projections and Least-Squares Problems
• The Gram-Schmidt process and QR-factorization
• The Singular Value Decomposition
• The condition number
• Tikhonov regularization

Applications

Examples of practical applications, including:

• Linear Regression (Principal Component Regression, Weighted Least Squares)
• Constrained optimization
• Principal component analysis
• Dynamical systems, including Markov chains
Learning outcome:

Knowledge

After completing the course, the student will understand important concepts in linear algebra, specifically under the points mentioned in the course content.

Skills The student will be able to apply the methods from the course to analyze models and solve practical problems where these methods are relevant.

General competence

The student will be able to use computer tools to do calculations and apply methods of linear algebra.

Learning activities:
The teaching is given as lectures (covering both theory and practical problemsolving) by the professor responsible for the course + exercise groups including both theoretical and practical problemsolving.
Teaching support:

Canvas is used for the course.

The students can also contact the teacher responsible for the course at the teacher's office, by telephone or by e-mail.

Syllabus:
Announced in Canvas.
Prerequisites:
MATH113 or MATH131
Recommended prerequisites:
MATH111, MATH112, basic skills in Python/NumPy or a similar programming language.
Mandatory activity:
No compulsory activities.
Assessment:
Final written exam, 3.5 hours.

Lectures: 54 hours

Plenary sessions: 24 hours

Weekly exercises: 72 hours

Self-study: 100 hours

Entrance requirements:
Special requirements in science
Reduction of credits:
10 credits reduction against MATH260
Type of course:
Lectures: 4 hours per week. Plenary session 2 hours per week.
Examiner:
The external and internal examiner jointly prepare the exam questions and the correction manual. The external examiner reviews the internal examiner's examination results by correcting a random sample of candidate's exams as a calibration according to the Department's guidelines for examination markings.
Allowed examination aids: B2 Calculator handed out, other aids as specified
Examination details: One written test: Letter grades