Daniel McKenzie

Daniel McKenzie

ML, signals and optimization

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Power weighted shortest paths for clustering Euclidean data

Published in Foundations of Data Science, 2019

We study the use of power weighted shortest path distance functions for clustering high dimensional Euclidean data, under the assumption that the data is drawn from a collection of disjoint low dimensional manifolds. We argue, theoretically and experimentally, that this leads to higher clustering accuracy. We also present a fast algorithm for computing these distances

Recommended citation: McKenzie, Daniel and Damelin, Steven. (2019). "Power weighted shortest paths for clustering Euclidean data. " Foundations of Data Science. 1(3). https://arxiv.org/abs/1905.13345

talks

teaching

Precalculus

Undergraduate course, University of Georgia, 2014

( 2014–2019 ) While a graduate student at UGA I taught Math1113, a one semester precalculus course, six times. The textbook we used was Precalculus by Julie Miller and Donna Gerkin. For some sections we used the ALEKS homework system. You can find a sample syllabus here and a copy of my first day of class slides here.

Calculus 1

Undergraduate course, University of Georgia, 2015

( 2014–2019 ) While a graduate student at UGA I taught Math2250, a one semester Calculus 1 course, three times. We used the textbook University Calculus, Early Transcendentals by Hass, Weir and Thomas. We used the WebWork homework system. I experimented with a variety of teaching modalities but one thing I found to be effective was creating a worksheet for every lesson which we would start in class and students would finish at home. You can find an example of such a worksheet here. You can find a copy of the syllabus here.

Calculus 3

Undergraduate course, University of California, Los Angeles, 2019

Math 32A is a large (~200 students), one-quarter course on multivariable calculus. Teaching Math 32A was an interesting experience as it involved giving auditorium-style lectures as well as managing a grader and three TA’s who met with the students in smaller groups. You can find a copy of the syllabus here. The reviews were mostly favorable.

Mathematics of Data Theory

Undergraduate course, University of California, Los Angeles, 2020

( 2019–2021 ) At UCLA I co-developed and then taught (three times) Math 118, an introduction to the mathematics of data science. I try to emphasize both theory and practice, so some lectures are slide-based presentations while others are more interactive and use Jupyter notebooks to play around with algorithms. I am happy to share my complete set of lecture slides, but not yet willing to make them completely public, so email me if you would like access.

Introduction to Statistics

Undergraduate course, University of California, Los Angeles, 2020

Math 170S is a mathematically rigorous introduction to statistics, using Hogg, Tanis and Zimmerman’s Probability and Statistical Inference . This was the first course I taught entirely over Zoom.

Honors Applied Numerical Methods

Undergraduate course, University of California, Los Angeles, 2021

At UCLA I co-developed Math151AH and Math151BH, honors versions of the pre-existing applied numerical methods classes. This two course sequence examines the theory and implementation of algorithms to solve fundamental problems in numerical analysis, for example least squares and SVD decompositions. We used this textbook. Here are the syllabi for Math151AH and Math151BH, and here is a sample lecture on one of my favorite algorithms, the power method.

Linear Algebra

Undergraduate course, Colorado School of Mines, 2022

40 person section of Linear Algebra.

Computational Linear Algebra

Graduate course, Colorado School of Mines, 2023

A graduate course on numerical linear algebra. We will focus on three fundamental problems; solving linear systems, solving least squares problems, and finding eigenvalues/eigenvectors. We’ll present multiple computational approaches for each. If you are a Mines student enrolled in this course you can find additional details on the canvas page.

Linear Algebra

Undergraduate course, Colorado School of Mines, 2023

40 person section of Linear Algebra. If you are enrolled in this class, please see our canvas page for further information.