MySchool

Tölvunarfræðideild
Deildarforseti:	Dr. Henning Arnór Úlfarsson
Vefpóstur:	ru@ru.is
Vefsíða:	http://www.ru.is/td
Kennarar	Skoða

BSc í tölvunarfræði
Annir:	6
Ár:	3
Einingar:	180
Um námsleiðina	Nám í tölvunarfræði hentar þeim sem langar að finna nýjar lausnir, eru skapandi og hugmyndaríkir og hafa áhuga á gervigreind, sýndarveruleika, stærðfræði, gögnum eða tölvuleikjum, svo dæmi séu tekin.
Hæfniviðmið	Skoða
Fagréttindi	Tölvunarfræðingur

Táknmyndir
	Skyldufag á braut		Kennslutungumál
	Valfag á braut		Undanfarar fyrir námskeið

Vorönn/Spring 2024

Viðskiptagreind

I-707-VGBI

6 Einingar

Forritun 1A

T-112-PRGA

2 Einingar

Forritun 1B

T-114-PRGB

2 Einingar

Forritun 1C

T-116-PRGC

2 Einingar

Gagnaskipan

T-201-GSKI

6 Einingar

Línuleg algebra með tölvunarfræði

T-201-LINC

6 Einingar

Ár

1. ár

Önn

Vorönn/Spring 2024

Stig námskeiðs

Óskilgreint

Tegund námskeiðs

Valnámskeið

Undanfarar

T-103-STST, Strjál stærðfræði fyrir verkfræðinema
T-111-PROG, Forritun
T-117-STR1, Strjál stærðfræði I

Skipulag

TD-Staðarnám - 12 vikna

Kennari

Christoph Lohrmann

Lýsing

A big portion (?) of modern technology is based on concepts from linear algebra, that are also essential in many areas of computer science, such as graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, bioinformatics, information retrieval and web search. Two basic elements of linear algebra are vectors and matrices. This course teaches the basics of vectors, matrices and algorithms based on them. The student will learn concepts and methods, how to work with them in Python as well as think about and solve various problems in computer science with linear algebra.

Námsmarkmið

Knowledge • Understand the basic concepts of linear algebra related to matrices, vectors and vector spaces. • Understand the terms linear combination, span and generating set. • Know what a basis is. • Know what similar matrices are. • Know what a diagonalizable matrix is. • Know what linearly dependent vectors are. • Understand the relationship between a diagonalizable matrix and linearly independent eigenvectors. • Understand orthogonal projections in many dimensions and orthogonalization. • Understand linear projections. • Understand the terms eigenvalue and eigenvector. • Know what QR factorization is. • Be familiar with linear algebra operations in Python. Skills • Can multiply vector and matrix, matrix and vector and two matrices. • Be able to determine whether vectors are linearly dependent. • Can find a generating set. • Can change a base. • Can solve linear equations with Gaussian elimination. • Can find the null set of an array and the solution set of a linear equation. • Can find the inverse of an invertible matrix. • Can use orthogonalization to find to find closest point and to solve other problems. • Can use the power method. • Can work with vectors and matrices in Python • Can solve problems with Python. • Be able to create programs in Python to implement matrix and vector algorithms, apply them to real data to solve various tasks such as analyzing and blurring faces and error-correcting code. Competences • Have the knowledge to look for the application of linear algebra in computer science. Have the knowledge to program solutions to linear algebra problems. • Have the knowledge to apply ´best approximation´ to solve various problems, e.g. image compression, least squares method, principal component analysis and information retrieval.

Námsmat

Ekkert skráð námsmat.

Lesefni

Ekkert skráð lesefni.

Kennsluaðferðir

Engin skráð kennsla.

Tungumál

Enska

Gagnasafnsfræði

T-202-GAG1

6 Einingar

Vefforritun