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SLG Presentations

Below I have some links to past presentations given by members of the group along with corresponding files (if available). The quality of the recordings is pretty low (this was before I really got into recording software) so please forgive that.

Fall 2014

Find the presentation files and links to recordings (if available) here. The presentations were:

• Neal Grantham: Intro to the Statistical Learning Group
• Justin Post: Intro to Statistical Learning
• Brian Gaines: Overview of Regularization
• Josh Day: Comparison of Regularization Parameters
• William Burton: Comparison of Regularization Parameters II
• Neal Grantham: Introduction to Classification
• Suchit Mehrotra: Bayesian Classification
• Munir Winkel: Trees
• Jami Jackson: Support Vector Machines
• Brian Naughton: Support Vector Machines for Ranking Models
• Huimin Peng: Support Vector Machines and Flexible Discriminant Analysis

Spring 2015

Find the presentation files and links to recordings (if available) for the first three talks here. The rest are linked on this repo in the list below. The presentations were:

• Alyson Wilson: Data Science Ideas
• Marschall Furman: Web Scraping in R
• Neal Grantham: Building a website with jekyll
• Chris Krut: Gaussian Processes in Machine Learning
• Justin Post: Shiny in RStudio (files, videos)
• Brad Turnbull: Text Mining (files, videos)
• Adam Suarez: R Tips & Tricks (files, videos)
• Suchit Mehrotra: Bayesian Methods for Missing Data (files, videos)

Fall 2015

• Neal Grantham: The Hitchhiker’s Guide to Deep Learning (files, videos)
• Dendi Suhubdy: Deep Learning for Images (files, videos)
• Alison Motsinger-Reif: Grammatical Evolution of Neural Networks for Genetic Epidemiology (files)
• Ajay Kumar: Ensemble Methods (videos)

Spring 2016

• Eric Chi: Overview of Variable Splitting Methods: ADMM and AMA (files)
• Marschall Furman: Using Google Maps in R (videos)
• Min Chi: Machine Learning and Data Mining with Applications to Educational Software

Fall 2016

• David Dickey: Introduction to Machine Learning (files, videos)
• Justin Post: Analysis of a Dataset and More with Trees (files, videos)
• Todd Wilson: Support Vector Machines (files, videos)
• Josh Day: Introduction to Julia (files, videos)
• Neal Grantham: Bayesian Inference Using Julia
• William Bauer: Philosophy of Information
• Isaac Michaud: Bayesian Optimization (files)
• Todd Wilson: apply() Yourself (files, videos)

Spring 2017

• Jessica Miller: Introduction to Hadoop (files, videos)
• Isaac Michaud: Parallel Computing in R (files)
• Kara Martinez: Is it Numbers? (files, videos)
• Jami Jackson Mulgrave: Gaussian Graphical Models (files, videos)
• Amanda Muyskens: Introduction to Spectral Methods (files)
• Marschall Furman: Introduction to Computer Vision (files, videos)

Fall 2017

• Todd Wilson: Machine Learning in the News, or, Why is Statistical Learning Important? (files, videos)
• Justin Post: Introduction to R Shiny (files)
• Isaac Michaud: Approximate Bayes Computation (files, videos)
• Pat Samranvedhya: Multivariate Mediation (files, videos)
• Joyce Cahoon: The purr Package and Docker Containers (files)