F21-STAT 441/841 CM 763-Proposal

From statwiki
Revision as of 21:21, 6 October 2020 by Cvmustat (talk | contribs) (Added Group 5 Proposal)
Jump to: navigation, search

Use this format (Don’t remove Project 0)

Project # 0 Group members:

Last name, First name

Last name, First name

Last name, First name

Last name, First name

Title: Making a String Telephone

Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).

Project # 1 Group members:

Song, Quinn

Loh, William

Bai, Junyue

Choi, Phoebe

Title: APTOS 2019 Blindness Detection


Our team chose the APTOS 2019 Blindness Detection Challenge from Kaggle. The goal of this challenge is to build a machine learning model that detects diabetic retinopathy by screening retina images.

Millions of people suffer from diabetic retinopathy, the leading cause of blindness among working-aged adults. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). In rural areas where medical screening is difficult to conduct, it is challenging to detect the disease efficiently. Aravind Eye Hospital hopes to utilize machine learning techniques to gain the ability to automatically screen images for disease and provide information on how severe the condition may be.

Our team plans to solve this problem by applying our knowledge in image processing and classification.

Project # 2 Group members:

Li, Dylan

Li, Mingdao

Lu, Leonie


Title: Risk prediction in life insurance industry using supervised learning algorithms


In this project, we aim to replicate and possibly improve upon the work of Jayabalan et al. in their paper “Risk prediction in life insurance industry using supervised learning algorithms”. We will be using the Prudential Life Insurance Data Set that the authors have used and have shared with us. We will be pre-processing the data to replace missing values, using feature selection using CFS and feature reduction using PCA use this processed data to perform Classification via four algorithms – Neural Networks, Random Tree, REPTree and Multiple Linear Regression. We will compare the performance of these Algorithms using MAE and RMSE metrics and come up with visualizations that can explain the results easily even to a non-quantitative audience.

Our goal behind this project is to learn applying the algorithms that we learned in our class to an industry dataset and come up with results that we can aid better, data-driven decision making.

Project # 3 Group members:

Parco, Russel

Sun, Scholar

Yao, Jacky

Zhang, Daniel

Title: Lyft Motion Prediction for Autonomous Vehicles


Our team has decided to participate in the Lyft Motion Prediction for Autonomous Vehicles Kaggle competition. The aim of this competition is to build a model which given a set of objects on the road (pedestrians, other cars, etc), predict the future movement of these objects.

Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. However, there are still significant engineering challenges to be solved before one can fully realize the benefits of self-driving cars. One such challenge is building models that reliably predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians.

Our aim is to apply classification techniques learned in class to optimally predict how these objects move.

Project # 4 Group members:

Chow, Jonathan

Dharani, Nyle

Nasirov, Ildar

Title: Classification with Abstinence


We seek to implement the algorithm described in Deep Gamblers: Learning to Abstain with Portfolio Theory. The paper describes augmenting the classic classification problem to include the option to abstain from making a prediction when uncertainty is high.

Project # 5 Group members:

Jones, Hayden

Leung, Michael

Haque, Bushra

Mustatea, Cristian

Title: Combine Convolution with Recurrent Networks for Text Classification


Our team chose to reproduce the paper Combine Convolution with Recurrent Networks for Text Classification on Arxiv. The goal of this paper is to combine CNN and RNN architectures in a way that more flexibly combines the output of both architectures other than simple concatenation through the use of a “neural tensor layer” for the purpose of improving at the task of text classification. In particular, the paper claims that their novel architecture excels at the following types of text classification: sentiment analysis, news categorization, and topical classification. Our team plans to recreate this paper by working in pairs of 2, one pair to implement the CNN pipeline and the other pair to implement the RNN pipeline. We will be working with Tensorflow 2, Google Collab, and reproducing the paper’s experimental results with training on the same 6 publicly available datasets found in the paper.