phd_resources

I'm currently pursuing my PhD in Statistics at the Hamilton Institute, in Maynooth, Ireland, and the focus of my research is Bayesian Machine Learning. This repository contains a wide diversity of links that I've found in any way useful for my current studies.

Bayesian stuff

• Case studies in Stan
• Bayesian Regression
• Statistical Rethinking
• BART
• BART by Andrew Parnell
• Doing Bayesian Data Analysis
• Survival Analysis in Stan
• MCMC tutorials
• Bayesian Neural Networks
• Laplaces Demon Archive
• PGMs
• PGMs 2
• PGMs 3
• David Blei's page
• Intro to Bayesian Stats, by Michael DeCrescenzo

Miscellaneous Machine Learning

• NN from scratch
• Probabilistic Graphical Models notes
• GLM notes
• Variable Selection by Andrew Parnell
• Feature Engineering
• Chris Albon's notes
• Visualisation references
• MCMC examples
• ML by Larry Wasserman
• Fast AI
• tidymodels
• MLSS 2019 London - Full Videos
• Isak's summary - MLSS 2019
• Variational Inference
• Mathematical Tours
• CS229 Stanford
• CS 294: Fairness in Machine Learning
• FMP Notebooks

Probability and Statistics

• Prob & Stats Cookbook
• Prob & Stats by Larry Wasserman
• Probabilistic modelling examples
• Measure Theory
• Measure Theory notes
• Measure Theory playlist

R

• Machine Learning with R
• Advanced Statistical Computing
• Laplaces Demon
• Data Visualization in the Tidyverse by Allison Hill
• Data Visualisation (Fronkonstin)

Julia

• Julia Scientific Programming - Course
• Tutorials

python

• Fluent python
• Visualisation
• Python for statisticians
• Method Chaining
• Bayes py
• Python for DS & AI

c++

• CS Fundamental courses
• Lecturer's github
• Introduction à la programmation orientée objet (en C++)

Coursera

• Bayesian Methods for Machine Learning
• Intro to Deep Learning
• NLP
• Practical Reinforcement Learning
• Probabilistic Graphical Models
• Information Theory

Interesting stats stuff

• Interpreting Cohen's d Effect Size

Anything else

• Screen to gif
• Paper writing
• Columbia's CS Ph.D. support page
• Presentation preparation
• YAML/rmd editing with glue

Conferences & Summer Schools (happen yearly)

Programming

• rstudio::conf
• UseR!
• pycon Ireland
• JuliaCon
• StanCon

Stats & ML

• 21 ML Conferences
• Global Women in Data Science Conferences
• Stats Conferences Calendar
• Machine Learning Summer School
• APTS
• Deep Learning Summer School
• Gaussian Processes Summer School
• Summer Schools List

Diversity Scholarships

• Why do they exist?
  • To increase diversity & inclusion in the tech environment
• What does it include, generally?
  • It can be either partial or full scholarships, including registration, accomodation, food and travel costs. It all depends on each conference/summer school rules.
• What to write in the application?
  • Your profile (where are you from, gender, what do you do now and plans to do later)
  • What is your current study/employment situation, emphasizing on how much going to the conference would be benefitial for it
  • Explain your budget limitations clearly (e.g. PhD students have almost no money for conferences/programming resources)
  • Explain how do you plan to "give" back what will be learned in the conference/summer school (e.g. share content on GitHub, apply it in your thesis or specific project)