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 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 which is giving page references to the course APPM 5270 at Univ. Texas, in particular this pdf file. also GVL_SVD.pdf under lecture notes.
Block 2: Numerical algorithms for clustering
- block2.pdf of which some parts are based on von Luxburgs tutorial
Block 3: Structured matrices in data analysis
- 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.

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:

SF2526 VT22 Numerical algorithms for data-intensive science (60346)