2023
SPR
MATH 396 Probability III
- Instructor: Andrea Ottolini
- Topics: Stochastic processing, Markov chain, hitting time, Gambler’s Ruin.
- Textbook: Finite Markov chains and Monte-Carlo methods By Soumik Pal and Tim Mesikepp
- Notes: [need update]
WIN
CSE 446 Machine Learning
- Instructor: Jamie Morgenstern
- Topics: Intro, Maximum Likelihood, Linear Regression, Overfitting, Regularization. Optimization. Classification. Non-linear models. Unsupervised Learning.
- Course Web: 2023 Winter CSE 446 UW
- Notes: [need update]
2022
AUT
CSE 415 Intro. to Artificial Intelligence
- Instructor: Steve Tanimoto
- Topics: Heuristic Search, Adversarial Search, Alpha-Beta Pruning, Expectimax Search, Markov Decision Processes, Value Iteration, Q-Learning, Perceptrons, Hidden Markov Models, NLP brief intro.
SUM
Economics Research
- Keywords: Econometrics, Causual Inferences, Neural Networks.
- Packages Used: PyTorch.
Mathematics Research
- Keywords: Elevator Allocation, Probability, Gibbs Random Field, Markov Chain.
- Paper: Elevator Optimization: Application of Spatial Process and Gibbs Random Field Approaches for Dumbwaiter Modeling and Multi-Dumbwaiter Systems
- Authors: Zheng Cao, Benjamin Lu Davis, Wanchaloem Wunkaew, Xinyu Chang
SPR
CSE 412 Intro. Data Visualizations
- Instructor: Professor Jon E. Froehlich
- Skills Learned: Tableau, p5.js, Vega-Lite
- Featured Project: Interactive visualization webpage using Observable involving javascript
Heart Disease Facts: indicators, risks, and myths
MATH 395 Probability II
- Instructor: Professor Zhen-Qing Chen
- Skills Learned: Distributions: Normal, Uniform, Poisson, and etc.
- Notes: Collection of Notes
STAT 416 Intro. Machine Learning
- Instructor: Pemi Nguyen
- Skills Learned: ML models(Lienar Regression, Ridge/Lasso Regression, Logistic Regression, Decision Tree, feature extraction with TfidfVectorizer, Neural Networks), Python
- Notes: Intro. Machine Learning Weekly Learning Reflections
- Featured Projects: Pick three models learned from the class to train an given Education Data.
Education Data Training by Xinyu(Xiyah) Chang, Jinxuan(Joanna) Yao, Zhiyu(Kelly) Wang
WIN
INFO 201 Data Science Foundations
- Instructor: David G Hendry (he/him)
- Skills Learned: R, Git Commands
- Featured Project: RShiny Webpage with Visualizations, using R to wrangle and clean data.
US Air Polution
2021
AUT
CSE 414 Database Systems
- Instructor: Ryan Maas
- Skills Learned: SQL, AZURE, Python
- Featured Project(s): Vaccine Scheduler. Completed ER Diagram, created tables, and implemented Python code combined with SQL commands. https://github.com/XiyahC/UwWorks/tree/master/CSE414VaccineSchedulerPythonSQL
ECON 411 Behavioral Economics
- Instructor: Salar Jahedi
- Highlight Contents: The usage of conditional probability in economics; Game Theory.
