steve303

NLP-SentimentClassification

Neural Network Models - Sentiment Classification of Tweets

spark_MLlib_graphf

Objectives: Using pyspark, MLlib and graphframes libraries, perform 1) classification and custering tasks using RandomF and Kmeans and 2) graph analysis tasks. This material is from UIUC MCS coursework.

stat542proj4-RecommenderSys

Build a Movie Recommender App - Algorithm Selection and App Implementation using R's Shiny Platform

AI_gym-MarkovDecisionProcess

Objective: Using the AI_gym environment design an algorithm which will instruct an agent to learn and succeed at different tasks

apacheStorm

Objective: Using Apache Storm construct a spout/bolt topology to perform a word count and report top N words. This is from course work at UIUC MCS.

dataviz-countryAdvancement

Data visualization D3.js - Country Advancement (education, health, employment)

DeepLearning-XrayClassify

Deep Learning Methods for Chest X-ray Classification

FEA-ML

Objective: This study investigates the use of finite element analysis (FEA) to generate data as an alternative to experimental data to train a machine learning (ML) algorithm for the purpose of creating a analytics tool to predict product manufacturing yield.

Kaggle03

german sign classification CNN

kuka_path_soln

marketAnalysis-LiquorStores

Objective: Perform market analysis on Iowa liquor stores using State of Iowa alcoholic records, US census, US geological GIS data.

multiThreadProg-sharedfiles

Objective: Create a multi-threaded program where shared files are protected and synchonized by mutex lock

RNN-prevalenceNLP

short article on the prevalence RNNs in NLP tasks

sparkSQL

Objective: Perform word count tasks and joins using spark SQL within a Docker container

stat420_logisticRegModels

stat420_variableSelection

stat542code1-ClassificationExamples

Objectives: 1) Design a KNN algorithm using R, 2) Summarize classification performance using KNN, linear/quadratic regression and Bayes rule. Training and test data are generated from a bi-variate Gaussian mixture model.

stat542code2-RegularizationMethods

Objectives: 1) Implement coordinate descent algorithm for lasso penalization using R, 2) Document performance differences between lasso and ridge penalization when performing regression on a reference data set versus reference data with added spurious predictors

stat542proj1-regressionTrees

Regression Models - Decision Trees (GBM) and Linear Regression (ElasticNet)

stent-nostent