# SF2526 Numerical algorithms for data-intensive science 7.5 credits

#### Numeriska algoritmer för vetenskapliga problem med stora datamängder

Due to a great and increasing importance in relevance of large-scale data in various fields in science and technology, there is a need to understand the computational approaches used to analyze, understand and extract information from large amounts of data. The course gives an introduction to the use of many efficient numerical algorithms associated arising in problems in the analysis of large amounts of data. We use mathematical and numerical tools to study problems and algorithms.

### Offering and execution

#### No offering selected

Select the semester and course offering above to get information from the correct course syllabus and course offering.

## Course information

### Content and learning outcomes

#### Course contents *

The course is mainly focused on the algorithmic and practical computational aspects in the following blocks.

• Numerical algorithms for data-intensive least squares problems
• Numerical algorithms for large graphs, networks and clustering
• Numerical algorithms for distance measures and classification

#### Intended learning outcomes *

After completing the course, the student will be familiar with important numerical methods and algorithms used to analyze data and describe when they are useful.

• The student should be able to independently identify and formulate the problems data-oriented problem classes presented in the course.
• The student will be select an appropriate algorithm to use to the solve the problems.
• The student should be able to describe algorithm properties and associate them with specific problem properties
• The student will be able to derive new variants and methods based generalizing methods in the course.

• Lectures
• Laborations

### Literature and preparations

#### Specific prerequisites *

Basic course in numerical analysis and computer science.

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology.  English B, or equivalent

#### Recommended prerequisites

No information inserted

#### Equipment

No information inserted

#### Literature

The course literature will be announced in the detailed course syllabus.

### Examination and completion

A, B, C, D, E, FX, F

#### Examination *

• LAB1 - Laboratory work, 3.5 credits, Grading scale: P, F
• TEN1 - Exam, 4.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The examiner decides, in consultation with KTHs Coordinator of students with disabilities (Funka), about any customized examination for students with documented,lastingdisability. The examiner may allow another form of examination for reexamination of individual students.

#### Other requirements for final grade *

• Laborations completed (LABA)
• Written Exam completed (TEN1)

#### Opportunity to complete the requirements via supplementary examination

No information inserted

#### Opportunity to raise an approved grade via renewed examination

No information inserted

### Further information

#### Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web SF2526

SCI/Mathematics

Mathematics

#### Education cycle *

Second cycle

No information inserted

#### Contact

Elias Jarlebring (eliasj@kth.se)

#### Ethical approach *

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.