STAT7614: Advanced Statistical Learning Mini Project

Aim

Recent years have witnessed a surge in Computer Science (CS) research, which
achieved unparalleled successes in a kaleidoscope of science and engineering
applications spanning artificial intelligence [1], natural language processing [2],
computer vision [3], statistical learning theory [4], blockchain and cryptocurrency
[5], computational biology [6] and bioinformatics [7], intelligent grids and Internetof-Things
(IoT) [8]. Consequently, the difficulty of pursuing a higher degree in
CS or related subjects in decent graduate schools has been elevated to an unprecedented
new height [9]. In this paper, motivated by these observations, the authors,
consisting of a data-driven earth scientist, a computational biologist, and a machine
learning researcher, study the factors governing the admissions of graduate schools
in the U.S. by means of Generalized Linear Models (GLM), Generalized Additive
Models (GAM), distribution-free methods such as Ensemble Learning (EL),
and Discrete Bayesian Networks (DBN). We seek answers to several interesting
questions and render crisp insights from the model inference and analysis. We hope
this paper may inspire future graduate schools applicants. We finally conclude this
paper by identifying limitations that are not addressed by this paper and pointing
out several possible future directions.

Delieverable

We use the paper draft philosophy introduced by Li-Yi Wei
throughout this project.
The report can be found online here.

Contributors

Chen Liu, Zehao Su, and Jiayao Zhang.
All authors contributed equally.

Cite this project

If you found this repo useful, please consider cite this project.
A possible bibtex entry may be:

@misc{LSZ18,
  author = {Liu, Chen and Su, Zehao and Zhang, Jiayao}
  title = {Understanding Admission Results of CS Graduate Programs in U.S. Universities},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/zjiayao/stat7614}},
}