2018年甜橙金融杯大数据建模大学_初赛——DC竞赛

A Bayesian Optimization Package with No Fuzz

I showcase that I have broad set of skills regarding machine learning algorithms since I use Logistic Regression, XGBoost and Neural Networks in this project. Especially that I have a good understanding regarding neural networks and the Keras library.

Finding the best solution for geometry of electrical devices based on defined target for frequency responses

A framework for exploring the condition space of free radical polymerization (FRP) using Bayesian optimization and Monte Carlo simulation

This notebook demonstrates timeseries classification for crop identification on a subset of the MiniTimeMatch dataset by training an LSTM model.

This repository is dedicated to the DataStorm Machine Learning Competition organized by John Keels Corporations and OCTAVE.

Different techniques to tune the hyperparameter of machine learning models.

Efficient and Scalable Batch Bayesian Optimization Using K-Means

Image Classification with Keras CNN

My undergraduate honours project, with others' private information/code removed.

Mind Foundry OPTaaS R Client

Bayesian Optimization for Machine Learning

Final Project about optimizing NN with Bayesian Optimization for Bayesian Methods in ML course in Washington University

Bayesian Optimization of SVM parameters with scikit-learn to be used in KNIME in Python-learner node

Nathan Rumsey's junior year International Science and Engineering Fair (ISEF) Project for the 2019-2020 school year.

Team programming exercises for the course unit "Probabilistic Artificial Intelligence", ETH Zurich

MultiObjective BO

A pointer to video lectures I gave as a teaching assistant for graduate level Applied Machine Learning

[Completed] Complete framework on multi-class classification covering EDA using x-charts and Principle Component Analysis; machine learning algorithms using LGBM, RF, Logistic Regression and Support Vector Algorithms; as well as Bayesian Optimizer with l1 and l2 regularization for Hyperparameter Tuning.