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https://github.com/calebmanicke/college_projects

Collection of my best papers and projects from my courses.

https://github.com/calebmanicke/college_projects

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Scientific vocabulary similarity
    Low similarity (6.1%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Collection of my best papers and projects from my courses.

Basic Info
  • Host: GitHub
  • Owner: CalebManicke
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 3.99 MB
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Created about 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme

README.md

This is a collection of what I believe are my best papers and projects from the courses I've taken. I provide a description of each below:

Idiot's Guide to Differential Equations: This was the final portfolio of the honors section of MATH 2410Q (Elementary Differential Equations) which I took freshman spring. Taught by Anthony Rizzie, this section was unique since the final portfolio was the only graded assignment in the entire class. Each page goes covers enough material and examples for a lecture of chapter in a textbook.

Leonhard Euler, The Greatest Mathematician: This was the fourth assignment from my MATH 2705W (Technical Writing in Mathematics) also taken freshman spring. The assignment was to give background behind a famous mathematician and thoroughly explain one key work of theirs.

Convex Hull: This was the honors project for CSE 3500 (Algorithms and Complexity) taken my sophomore spring. Out of three assignments, I chose to implement an algorithm that when given a set of points, would return the convex hull using a divide and conquer paradigm.

Stochastic Optimization for Machine Learning: This was the final project for MATH 3160 (Elementary Stochastic Processes) also taken sophomore spring. The assignment was to pick and explain any example of a stochastic process in other applications, so I chose to give a rigorous proof behind how when given enough iterations, stochastic gradient descent is bound to converge to an optimal set of weights.

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  • Login: CalebManicke
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