## Announcements

- The list of lectures is on this google sheet. The schedule will be updated throughout the schedule -- I will try to accurately estimate what topic the lecture is for the next class, but topics further out are less certain due to variances in how long it takes to cover a subject
- Code from demonstrations will be on the github repo site (homework is also posted there as well)
- The syllabus is available (syllabus version Wed Jan 16, syllabus version Fri Jan 11)
- A brochure advertising the course

### Quick Links

## Course Information

**Text:** *There is no required textbook*

### Resources (in place of a textbook):

Specific papers will be mentioned in a separate place, but here is a list of monographs that are helpful:

- Gunnar Martinsson previously taught a version of this course, and still has information on the course website Fast algorithms for big data from spring 2016.
- Gunnar Martinsson also has a 34 page monograph Randomized methods for matrix computation (started 2016, updated 2018)
- Roman Vershynin published a Nov 2018 300 page book High-Dimensional Probability: An Introduction with Applications in Data Science published by Cambridge University Press
- Like many books from Cambridge University Press, they are allowing Vershynin to host a PDF copy on his website.

- David P. Woodruff has a 157 page monograph Sketching as a Tool for Numerical Linear Algebra from 2014 as part of the Foundations and Trends® in Theoretical Computer Science series.
- You can access the text via the arXiv version

- Michael Mahoney has a 100 page monograph Randomized algorithms for matrices and data from 2011 as part of the Foundations and Trends® in Machine Learning series
- You can access the text via the arXiv version

- Lectures on Randomized Numerical Linear Algebra, by Michael Mahoney and Petros Drineas, 2017, "This chapter is based on lectures on Randomized Numerical Linear Algebra from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data."

### Textbooks for related topics

- for numerical linear algebra,
- Applied Numerical Linear Algebra by James Demmel (SIAM 1997). You have free access to a PDF version of each chapter if you are on CU campus

## Lecture Times and Location

Instructor | Room Number | Time |
---|---|---|

Stephen Becker | FLMG 102 | MWF 10 to 10:50 |

## Office Hours

Instructor/TA | Room Number | Office Hours |
---|---|---|

Stephen Becker | ECOT 231 | Wed 4 to 5 PM; Thurs 1 to 2 PM and 4 to 5 PM |

## Homeworks

**Homework solutions are to uploaded to Canvas. **

Homework | Due date |
---|---|

HW 1 (reading) | Friday Jan 18, 2019 |

HW 2 (math and code) | Friday Jan 25, 2019 |

HW 3 (reading and code) | Friday Feb 1, 2019 |

HW 4 (math and code) | Friday Feb 8, 2019 |

HW 5 (math and code) | Friday Feb 15, 2019 |

HW 6 (reading) | Friday Feb 22, 2019 |

HW 7 (reading and code) | Friday Mar 1, 2019 |

HW 8 (reading, math and code) | Friday Mar 8, 2019 |

HW 9 (code) | Friday Mar 15, 2019 |

HW 10 (code) | Monday April 1, 2019 |

## Exams

There are no exams (no midterms, no finals)

## Projects

Details about any class projects, TBD