Deep Learning for Big Data

Deep Learning Tutorial

Beginning deep learning with 500 lines of Julia

The link.

Markov and Hidden Markov Model Tutorial

Tutorial

Large Open Data Sets

Link 1 which is a list of large open data sets.
Link 2 - Common Crawl Corpus Amazon
Link 3 - Memtracker
Link 4 - Apache Access Logs
Link 5 - Clueweb09 Wiki
Link 6 - Click dataset
Link 7 - REDD
Link 8 - NASA
Link 9 - Dartmouth Atlas of Health Care
Link 10 - Data.gov Catalog
Link 11 - Data.gov Catalog - Complete
Link 12 - Awesome Public Datasets

Papers that discuss Wild Big Data

Data in the Wild

EM Tutorial

EM Tutorial

Using PageRank to Locally Partition A Graph

Using PageRank to Locally Partition A Graph

An Introduction to Conditional Random Fields

CRF Examples...

t-SNE

t-Distributed Stochastic Neighbor Embedding (t-SNE)

A Brief Introduction to Graphical Models and Bayesian Networks

http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html

AI is awesome

http://dataconomy.com/eve-stumbles-upon-a-possible-cure-for-malaria-eve-is-a-robot-scientist/

Jeremy Howard: The wonderful and terrifying implications of computers that can learn

Advanced Machine Learning with scikit-learn

My first teaching experience

I would have to say it was definitely a learning experience. I taught a database course to 40 mostly seniors and mostly male students. I spent the semester researching and learning which techniques were best used for teaching. I also learned how to create my own lectures, projects and exams. This experience definitely gave me a taste of what it would be like to teach professionally. I also learned a bit about myself. By the end of the semester I no longer felt uncomfortable with speaking in front of a group or answering questions and challenges on the fly. I also was impressed with many of the students in my class.

Now it is back to research!

And maybe teaching again in the future once I recover from this first one.

Taming Wild Big Data

Our latest paper for the AAAI Fall Symposium.
Abstract: Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.

Confidence in Lasso by Jared Lander

Confidence in Lasso by Jared Lander.

Learning Julia

julia is a dynamic programming language getting a bit of attention. I am running a few tutorials and learning the language. Some resources are listed below in case you are interested....

Learn about julia
Quick tutorial
Another tutorial
Google group

Lego Car

This is just too cool, a lego car tha runs on compressed air.

Kano kit