My teaching advice and resources (so far)

As I general rule I find thinking about math pedagogy deeply rewarding. Teaching a technical and beautiful discipline like math is difficult to do well. Students come from all sorts of backgrounds, the material can be challenging, and there are tons of moving parts in a course. It’s a challenge that I find exhilarating.

On the other hand, I find the act of reading the scholarship of math education to be dreadful and unpleasant. It is filled with jargon and hero-worship.

That being said, I’ve been extremely lucky to have great mentors and colleagues to bounce ideas off of. I’ve collected some of this advice in a Reddit post, which I’ll recreate here.

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How does modern AI work? – Math for my mom

This is part of a series of posts aimed at helping my mom, who is not a scientist, understand what I’m up to as a mathematician.

 

Lately, Artificial Intelligence (AI) has made some remarkable milestones. There are computers that are better than humans at the strategy board game GO and at Poker. Computers can turn pictures into short moving clips and can “enhance” blurry pictures as in television crime shows. They can also produce new music in the style of Bach or customized to your tastes. It’s all very exciting, and it feels pretty surreal; remember back when Skype video calling felt like the future?

I’m going to give you a broad overview for how these types of AI work, and how they learn. There won’t be any equations or algebra.

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Euclidean Ramsey Theory 2 – Ramsey DocCourse Prague 2016

The following notes are from the Ramsey DocCourse in Prague 2016. The notes are taken by me and I have edited them. In the process I may have introduced some errors; email me or comment below and I will happily fix them.

Title: Euclidean Ramsey Theory 2 (of 3).

Lecturer: David Conlon.

Date: November 25, 2016.

Main Topics: Ramsey implies spherical, an algebraic condition for spherical, partition regular equations, an analogous result for edge Ramsey.

Definitions: Spherical, partition regular.

Lecture 1 – Lecture 2 – Lecture 3

Ramsey DocCourse Prague 2016 Index of lectures.

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Dual Ramsey, an introduction – Ramsey DocCourse Prague 2016

The following notes are from the Ramsey DocCourse in Prague 2016. The notes are taken by me and I have edited them. In the process I may have introduced some errors; email me or comment below and I will happily fix them.

Title: Dual Ramsey, the Gurarij space and the Poulsen simplex 1 (of 3).

Lecturer: Dana Bartošová.

Date: December 12, 2016.

Main Topics: Comparison of various Fraïssé settings, metric Fraïssé definitions and properties, KPT of metric structures, Thick sets

Definitions: continuous logic, metric Fraïssé properties, NAP (near amalgamation property), PP (Polish Property), ARP (Approximate Ramsey Property), Thick, Thick partition regular.

Lecture 1 – Lecture 2 – Lecture 3

Ramsey DocCourse Prague 2016 Index of lectures.

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Bootcamp 1 – Ramsey DocCourse Prague 2016

The following notes are from the Ramsey DocCourse in Prague 2016. The notes were taken and edited by myself and Michael Kompatscher. In the process we may have introduced some errors; email us or comment below and we will happily fix them.

Title: Bootcamp 1 – Informal meeting.

Lecturer: Jaroslav Nešetřil.

Date: September 20, 2016.

Main Topics: Overview over the topics of the DocCourse; classical result in Ramsey theory

Definitions: Arrow notation, Ramsey numbers, arithmetical progression

Bootcamp 1 – Bootcamp 2Bootcamp 3Bootcamp 4Bootcamp 5Bootcamp 6Bootcamp 7Bootcamp 8

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