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links.jemdoc
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# jemdoc: menu{MENU}{link.html}
= Useful Links
~~~
This page aims to record some useful links that help our study and research, it will keep updating.
\n
If there were any error, please kindly contact me to correct it, thank you very much.
~~~
== Suggestions on Graduate Study
- [http://personal.cityu.edu.hk/mchen88/advice.html A List of Suggestions to Graduate Students] by Prof. Minghua Chen @CityU
- [http://karpathy.github.io/2016/09/07/phd/ A Survival Guide to a PhD] by Andrej Karpathy @Tesla
- [https://cs.uwaterloo.ca/~thachisu/survival.pdf Graduate Study Survival Guide (PDF)] by Toshiya Hachisuka @UWaterloo
- [http://www.stat.cmu.edu/~aramdas/checklists.html Checklists for Stat-ML PhD students] by Aaditya Ramdas @CMU
- [https://xinmingtu.cn/blog/2022/Academic-resource Some resources related to the PhD journey] by Xinming Tu @UW
- [https://sites.google.com/view/econgradadvice/ Advice for Phd Students in Economics] by Chris Roth @UoC and David Schindler @Tilburg
== Selected Course and Tutorial Websites Related to Optimization
- [http://web.stanford.edu/~boyd/ Several courses on optimization] by Prof. Stephen Boyd @Stanford
- [http://www.seas.ucla.edu/~vandenbe/index.html Several courses on optimization] by Prof. Lieven Vandenberghe @UCLA
- [https://sites.google.com/view/cjin/home Several courses on optimization, machine learning and RL] by Prof. Chi Jin @Princeton
- [http://bicmr.pku.edu.cn/~wenzw/index.html Several courses on optimization] by Prof. Zaiwen Wen @PKU
- [http://yintat.com/teaching.html Several courses on optimization] by Prof. Yin Tat Lee @UW
- [http://www.stat.cmu.edu/~ryantibs/teaching.html Several courses on optimization, machine learning and statistics] by Prof. Ryan Tibshirani @CMU (also resources therein)
- [https://gowerrobert.github.io/ Several courses on optimization & machine learning] by Prof. Robert M. Gower @Telecom Paris
- [https://yuxinchen2020.github.io/ele522_optimization/ Large-Scale Optimization for Data Science (Fall 2019)] by Prof. Yuxin Chen @Upenn
- [http://www.cs.umd.edu/class/fall2020/cmsc828W/ Foundations of Deep Learning (Fall 2020)] by Prof. Soheil Feizi @UMD
- [https://www.di.ens.fr/~fbach/learning_theory_class/ Learning Theory from First Principles] by Prof. Francis Bach @INRIA
- [http://www.cs.toronto.edu/~rgrosse/teaching.html Several courses on optimization, machine learning] by Prof. Roger Grosse @UToronto
- [https://www.epfl.ch/labs/lions/teaching/ee-556-mathematics-of-data-from-theory-to-computation/ Mathematics of Data: From Theory to Computation] by Prof. Volkan Cevher @EPFL
== Useful Resources on Optimization and Machine Learning
- [http://sunju.org/research/nonconvex/ Provable Nonconvex Methods/Algorithms] by Prof. Ju Sun @UMN
- [https://arxiv.org/search/?query=math.OC&searchtype=all&order=-announced_date_first&size=50 Math.OC] Optimization and control papers in arXiv
- [https://arxiv.org/search/?query=stat.ML&searchtype=all Stat.ML] Machine learning and statistics papers in arXiv
- [https://www.youtube.com/user/SimonsInstitute/playlists Lecture video series] in Simons Institute for the Theory of Computing, UC Berkeley
- [https://csml.princeton.edu/bridgingmathematicaloptimizationslidesvideos Lecture videos and slides] of the "Bridging Mathematical Optimization, Information Theory, and Data Science" workshop at Princeton, May 2018.
- [http://www.columbia.edu/~ck2945/post/reading-list/ Reading List] on common math and optimization used in research by Prof. Christian Kroer @Columbia
- [https://jinmingxu.github.io/summer_school2021.html Lecture videos and slides] of the ZJU-CSE Summer School 2021
== Useful Resources on Mathematics and Statistics
- [http://www.cs.cmu.edu/~zkolter/course/linalg/index.html Linear Algebra Review] by Zico Kolter @CMU
- [http://www.matrixcalculus.org/ Matrix Calculus]
== Academic Conferences and Selected Online Seminars
- [https://aideadlin.es/?sub=ML A collection of countdown of major machine learning conference deadlines]
- [https://researchseminars.org/ A collection of recent research seminars]
- [https://owos.univie.ac.at/ One World Optimization Seminar]
- [https://www.oneworldml.org/home One World Seminar Series on the Mathematics of Machine Learning]
- [https://sites.google.com/view/seminarmathdatascience/home Online Seminar on Mathematical Foundations of Data Science]
== Research & Academic Management
- [http://luc.devroye.org/commandments.html Ten commandments of authorship] by Prof. Luc Devroye @McGill
- [https://www.aaai.org/ojs/index.php/AAAI/article/download/5028/4901 A short summary of research group management using online tools] by Prof. Eric Eaton @UPenn
- [https://mmcheng.net/docx/ DOCX (in Chinese)] by Prof. Ming-Ming Cheng @Nankai
- [https://yisongyue.medium.com/checklist-of-tips-for-computer-science-faculty-applications-9fd2480649cc Tips for Computer Science Faculty Applications] by Yisong Yue @Caltech
- [https://www.kiragoldner.com/blog/index.html The Strategy Space] by Kira Goldner @BU
== Mathematical Tools
- [https://www.desmos.com/calculator Desmos | Graphing Calculator]
- [http://detexify.kirelabs.org/classify.html Detexify | Recognize symbols of LaTeX]
== About the Webpage: jemdoc
jemdoc is a light text-based markup language designed for creating websites, created by [https://jemnz.com/ Jacob Mattingley]. It is very suitable for academic style website, which makes your website clear and concise.
- [https://szl2.github.io/jemdoc-new-design/www/cheatsheet.html Cheatsheet of jemdoc].