Jack Yang

Computer Science and Math Student at UC San Diego, Class of 2023

About Me

Hello! I'm Jack, a second-year Computer Science and Math student at University of California, San Diego. I will be a tutor for CSE 20 Introduction to Discrete Mathematics here at UCSD during the Spring 2021 quarter. My resume is via here.

My academic interests lie in Probability (especially in Markov Chain, Tail Bounds, and Limit Theorems), Artificial Intelligence, and Machine Learning (yes, Hidden Markov Model).

Here you'll be able to check out a few things that I've been up to during my time as a student here at UC San Diego and whatever I come up with during my free time.

Life is like a Markov chain, your future only depends on what you are doing now, and independent of your past.



Course Work and Academic Performance at UCSD

GPA

  • Undergraduate GPA: 3.9
  • Computer Science Major GPA: 4.0
  • Math Major GPA: 3.9

Computer Science

Math - Probability and Statistics

  • Math 20C Calculus & Analytic Geometry For Science & Engineering
  • Math 20D Introduction to Differential Equations
  • Math 20E Vector Calculus
  • Math 31A Honors Linear Algebra
  • Math 109 Mathematical Reasoning
  • Math 140A Foundations of Real Analysis I

    My notes is available here. If you find any theoretical mistake, feel free to contact me via yanggangjack@gmail.com.

  • Math 140B Foundations of Real Analysis II

    My notes is available here. If you find any theoretical mistake, feel free to contact me via yanggangjack@gmail.com.

  • Math 140C Foundations of Real Analysis III (Planning - Spring 2021)
  • Math 180A Introduction to Probability
  • Math 180B Introduction to Stochastic Processes I
  • Math 180C Introduction to Stochastic Processes II (Planning - Spring 2021)
  • Math 181A Introduction to Mathematical Statistics I
  • Math 181B Introduction to Mathematical Statistics II (Planning - Spring 2021)
  • Math 184 Enumerative Combinatorics

    My notes is available here. If you find any theoretical mistake, feel free to contact me via yanggangjack@gmail.com.

General Education

  • ECON 2 Market Imperfections & Policy
  • HILD 2C United States History Since the Progressive Era
  • MCWP 40 Critical Writing
  • MCWP 50 Critical Writing
  • MUS 1A Fundamentals of Music A
  • MUS 1B Fundamentals of Music B
  • MUS 1C Fundamentals of Music C (Planning - Spring 2021)
  • PHYS 2C Fluids, Waves, Thermodynamics, and Optics

Projects

Titanic: Machine Learning from Disaster

September 2020


The most famous competition over the Kaggle. In this repository, I presented my thought on data handling, data cleaning, and data exploring. For data analysis, I used Decision Tree to predict the survival of rest of the travelers.

Dependencies

  • Python3

  • Numpy

  • Pandas

  • Matplotlib

  • Scikit-learn

Talks

Last quarter (Fall 2020), I am in Professor Ery Arias-Castro's Directed Reading Program (Math 199 at UCSD). I will give biweekly presentations (talks) in the group meeting.

  • On October 13, I give a talk about Linear Discriminant Analysis. (I have a bit mess up about the content. I shouldn't put the Naive Bayes in it.) The slide is via here. Toward the application, I have a Jupyter Notebook for the Naive Bayes via here. The dataset about the Spam texts is via here (originally from Kaggle). Another dataset helps me clean the raw texts is via here.
  • On October 27, I give a talk about Decision Tree. The slide is via here.
  • On November 17, I give a talk about Bias-Variance Tradeoff. Also, I further present the Random Forest, an extension and improvement of the Decision Tree and a concrete example of Bias-Variance Tradeoff. The slide is via here.
  • On November 24, I give a talk about Linear Programming from a geometric perspective, some related theorems about existence of solutions, and the Simplex Algorithm. The slide is via here. A detailed example and proof of some theorems are in this file.
  • On December 8, I continue Linear Programming, analyze the runtime of Simplex Algorithm, present Interior Point Method, and give an application of Linear Programming in Uniform Most Powerful Test. The slide is via here. The video recording is via here.

Teaching

Undergraduate Instructional Assistant (Tutor)

Contact

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