Read Travis Barton’s article about teaching a language model to play Chess!
LilRhino has reached 10,000 downloads 🎉
NLP and ML package ‘LilRhino‘ (authored by Travis Barton) has reached over 10,000 downloads on cran!
That’s 10,000 machines that have the ability to apply quality of life additions, advanced text pipelining, and automated ML applications!
“For” all my python users, here’s an interesting blip on writing really clean loops!
Naturally clean and readable code is one of the nicest features of python, but it’s notorious for inefficient loops. This article covers topics in both of those areas, check it out!
Michael Onuoha Predicts the Election
Coming SJSU grad Michael Onuoha used 538’s aggregate statistics dataset and to simulate and predict the winner of the 2020 presidential election. Check out his work and resume linked below.
who will win the 2020 presidential election?
Michael is currently enrolled in San Jose State University (SJSU) as an undergraduate student with a major in applied mathematics with a concentration in economics and actuarial science. His areas of interest include statistical and machine learning, model fitting, linear regression, and data visualization. Michael’s career goal is to acquire a career in data science to gather, analyze, and manipulate data and to draw conclusions accordingly. He wants to use large complex datasets to construct algorithms to build predictive mathematical models.
Michael’s favorite hobbies include reading and watching sports such as basketball and football. Being an undergraduate student in the Silicon Valley, he sees how technology plays a key role in people’s daily lives and aspires to contribute to the development of his surrounding community and society as a whole.”
LinkedIn link: https://www.linkedin.com/in/michael-onuoha-0a9359180
Resume link: Michael’s Resume
Congrats Maddy, Terris and Kevin for completing your masters degrees!
This December three of our members completed their masters degrees in statistics from San Jose State University.
Kevin, Maddy and Terris are actively looking for work in the bay area, so please check out their pages for more information on their work!
LilRhino, a must-have tool for NLP Programers!
Member Travis Barton has released his new major update to LilRhino, an R package for NLP and code convenience. It features pretreatment tools, as well as a special classifier called ‘feed reduction networks’, and code convenience functions. Check it out on CRAN!
Stay tuned for automatic GLoVe word vectorizer functions, as well as a Python version!
Travis Barton’s Masters Thesis has been completed!
WBB Predictions would like to congratulate Travis Barton for the completion of his Masters program in statistics! This was a two year effort that cumulated in his Master Thesis: Distinguishing Phylogenetic Networks With Machine Learning. He is now looking for work in Data Science and Machine Learning, so keep your eyes open for more of his work!
Abstract:
The reconstruction of phylogenetic structures is a classic applied biology pursuit and is the main focus of many researchers in the field of evolutionary biology. While traditionally, these structures were assumed to be trees, recent opinion amongst phylogeneticists is that network structures may sometimes be a better fit (network structures meaning either a non-tree graph or a collection of multiple trees). This introduces a new problem, however, namely which structure best describes the evolutionary history of a set of species. The purpose of this thesis is to shed some light on that topic, suggesting our own decision algorithm that classifies a set of aligned DNA sequences from four species into a tree or network structure. We select one of 21 labeled four-leaf networks with a combination of two different machine learning techniques. First, we use k-nearest neighbors to select the topology, and then use support vector machines to select the labeled network in that topological class.
Interested in the Monty Hall Problem? Check out this simulation!
Travis Barton presents a walk through of the famous Monty Hall problem!
The Redditbot is here!
Welcome to the world AskScienceBot!
WBB Predictions are proud to announce the launching of member Travis Barton’s passion project, the r/askscience Redditbot! Its a nifty little program (the first of many) that will be working to automate Reddit processes! Its goal is to predict the tags of the post from the popular subreddit r/askscience.
It uses Google’s Auto ML textual analysis platform to classify posts, and is run on an AWS ec2 instance, with an s3 storage backup.
See it in action by posting ‘Hey asksciencebot, report!’ in the subreddit r/TravsBots, or subscribe and wait for its weekly updates. It reports every Monday!
To find out more about it, and a new variable reduction technique, check out a short paper authored by Travis.
What’s New: Fall 2018
The team at WBB Predictions is happy with our summer work of delivering some tutorials on famous problems in machine learning. We are also thrilled with the website as a whole and are happy to announce the edition of a new logo and color scheme (coming soon!). As we start a new semester in our Master’s program, our focus will shift from tutorials to original work. Please be on the lookout for updates and completed versions of the following projects.
1) A fully functioning web application that recommends movies it thinks you will enjoy.
2) A machine learning bot that predicts the success of reddit posts.
3) R packages.
4) Reasearch/class projects.
The founding members of WBB Predictions are also starting a data science club at San Jose State University. We are excited to be sharing our passion for statistics, programming, and data science with both graduate and undergraduate students at SJSU. We think the club will lead to some exciting projects and networking opportunities within the data science community of Silicon Valley. If you are interested in joining the club or attending one of our networking events, contact any of the members of WBB Predictions.