Numerical Methods for Computational and Data Sciences Amnir Hadachi - - PowerPoint PPT Presentation

Numerical Methods for Computational and Data Sciences

Amnir Hadachi and Benson Muite

amnir.hadachi@ut.ee and benson.muite@ut.ee

https://courses.cs.ut.ee/2015/Num_M/spring

12 February 2015

Course Aims

• Learn how linear algebra on single processor, multicore

and possibly distributed memory machines can be used for data analytics applications

Course Overview

• Lectures Monday J. Livii 2-512 Tentative? 10.15-12.00

Amnir Hadachi and Benson Muite

• Practical Tuesday J. Livii 2-??? Tentative? 14.15-16.00

Amnir Hadachi and Benson Muite

• Homework/Mini projects typically due once every 2-3
• weeks. Expected to start this in the labs.
• Exams will be scheduled in later in semester – likely to be

viva/oral exam

• Final projects due and project presentations in examination

period in June

• Grading: Computer classes 35%, Exam/Quizz 25%,

Project 30%, Class participation 10%

• Course Texts: Listed on website, readings to also be

provided

Lecture Topics

• Overview of linear algebra
• Examples using Python
• Direct methods: Dense LU decomposition, LINPACK,

Sparse LU decomposition

• Eigenvalues SVD, QR factorization
• Least squares
• Iterative methods: Conjugate gradient method, multigrid

method

• Filtering Algorithms: Kalman filter, particle filter, monte

carlo filter

• Data science applications

Organization

• Small introductory lecture component
• Reading component (one leader each week - expected to

provide a summary and discussion points)

• Labs to implement some of the algorithms in the readings

Areas of Interest for Amnir

• Image processing
• Machine learning
• Shortest Path Problem
• Travel Time Estimation and Prediction
• Tracking and Location Estimation

Areas of Interest for Benson

• Analysis of computer logs
• Automated image recognition
• Consumer purchasing behavior
• Data analytics computer systems (https:

//www.tacc.utexas.edu/systems/wrangler)

• Estonian economy model
• Estonian statistics (see http://www.stat.ee/)
• Optimal Estonian taxation (see www.research.

stlouisfed.org/wp/2014/2014-017.pdf)

• Search engines and web page rankings
• Secure data storage
• Wikipedia logs (https://wikitech.wikimedia.org/

wiki/Analytics/Pagecounts-raw)

Other possible topics of interest

• Graph 500 list and benchmark
• Distributed memory programming for search applications

Action Items

• Please get an account on Rocket

http://www.hpc.ut.ee/

• Take a look at the topics covered in the suggested course

texts

• Send us a one page summary of your data science and

linear algebra interests by 23:59 Sunday 15 February