Welcome to the course
SF2526 - Numerics for data science
Numerical algorithms for data-intensive science
Data analysis of large data sets is increasing in importance as a new tool in many fields in science and technology. This course gives an introduction to the use of many efficient numerical algorithms arising in problems in the analysis of large amounts of data. We use mathematical and numerical tools to study problems and algorithms.
Want to get started: Try Quiz 0: Background and a bit and check out hw1.pdf under files.
The zoom room for lectures/workshop can be found in the welcome announcement.
Want help with homework, quizzes or active learning? You can contact teaching assistant Joar Bagge: joarb @ kth punkt se
Course contents and literature:
Before the course starts we recommend you review linear algebra summarized in background.pdf Download background.pdf and also do Quiz 0: Background and a bit.
The course consists of three blocks (the PDF-files are preliminary and will be updated)
- Block 1: Low-rank data algorithms
- block1.pdf Download block1.pdf which is giving page references to the course APPM 5270 Links to an external site.at Univ. Texas, in particular this pdf file. Links to an external site.also GVL_SVD.pdf under lecture notes.
- Block 2: Numerical algorithms for clustering
- block2.pdf Download block2.pdf of which some parts are based on von Luxburgs tutorial Links to an external site.
- Block 3: Structured matrices in data analysis
- block3.pdf Download block3.pdf and GvL_chapt4.pdf + bjorck.pdf + PGM_chapter5_fast_direct_solvers.pdf
What do I need to do?
Mandatory for the course:
- Homework 1: Homework 1 the data files are available here.
- Homework 2: Homework 2. Instructions on how to use similarity graphs: Similarity graphs
- Homework 3: Homework 3
- Complete all canvas assignments marked called "Activity" (See course plan) to be completed not later than one week after the exam (the CANVAS-deadlines are recommendations).
- Cancelled: Attend the workshops (in classroom or via zoom) or do the alternative task (See workshop instructions)
- Exam
Not mandatory, but can give additional bonus (See further description on the page for Homework / bonus points rules)
- Do the work on the Active learning workspace.
Active learning workspace
Throughout the course you have the possibility to train the material and get teacher feedback. This is done on the active learning workspace.
- Instructions for the active learning workspace
- block 1 training area
- block 2 training area
- block 3 training area
The work in the course training area is mainly intended for your own learning, and it can also lead to additional bonus points as described on Homework / bonus points rules
Course plan
Lecture/workshop slides are available here (only accessible for registered students).
More information: