awesome-h2o
by h2oai
A curated list of research, applications and projects built using the H2O Machine Learning platform
AI summary
H2O project list
A curated collection of projects and content utilizing the H2O Machine Learning platform
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What's in the list
94 links in 7 sections, with live GitHub stats.activeno commit in 2y
Blog Posts & Tutorials
- Parallel Grid Search in H2O
Jan 17, 2020
- Inspecting Decision Trees in H2O
Nov 07, 2018
- Gentle Introduction to AutoML from H2O.ai
Sep 13, 2018
- Time series machine learning with h2o+timetk
Oct 28, 2017
Books
- Big data in psychiatry and neurology, Chapter 11: A scalable medication intake monitoring system
Diane Myung-Kyung Woodbridge and Kevin Bengtson Wong. (2021)
- Hands on Time Series with R
Rami Krispin. (2019)
- Mastering Machine Learning with Spark 2.x
Alex Tellez, Max Pumperla, Michal Malohlava. (2017)
- Machine Learning Using R
Karthik Ramasubramanian, Abhishek Singh. (2016)
- Disruptive Analytics
Thomas Dinsmore. (2016)
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
Bradley Efron, Trevor Hastie. (2016)
- R Deep Learning Essentials
Joshua F. Wiley. (2016)
- Spark in Action
Petar Zečević, Marko Bonaći. (2016)
- Handbook of Big Data
Peter Bühlmann, Petros Drineas, Michael Kane, Mark J. van der Laan (2015)
Research Papers
- Automated machine learning: AI-driven decision making in business analytics
Marc Schmitt. (2023)
- Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques
Hamza Ahmad Madni, Muhammad Umer, Abid Ishaq, Nihal Abuzinadah, Oumaima Saidani, Shtwai Alsubai, Monia Hamdi, Imran Ashraf. (2023)
- Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth
Padmavati Kulkarnia, V.Sreekantha, Adithi R.Upadhyab, Hrishikesh ChandraGautama. (2022)
- Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department
Noa Galtung, Eva Diehl-Wiesenecker, Dana Lehmann, Natallia Markmann, Wilma H Bergström, James Wacker, Oliver Liesenfeld, Michael Mayhew, Ljubomir Buturovic, Roland Luethy, Timothy E Sweeney , Rudolf Tauber, Kai Kappert, Rajan Somasundaram, Wolfgang Bauer. (2022)
- Depression Level Prediction in People with Parkinson’s Disease during the COVID-19 Pandemic
) Hashneet Kaur, Patrick Ka-Cheong Poon, Sophie Yuefei Wang, Diane Myung-kyung Woodbridge. (2021)
- Machine Learning-based Meal Detection Using Continuous Glucose Monitoring on Healthy Participants: An Objective Measure of Participant Compliance to Protocol
Victor Palacios, Diane Myung-kyung Woodbridge, Jean L. Fry. (2021)
- Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth
Alex Luna, Joel Bernanke, Kakyeong Kim, Natalie Aw, Jordan D. Dworkin, Jiook Cha, Jonathan Posner (2021)
- Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of Endoscopic Surgery and new technologies (the CRAC study)
Alberto Sartori, Mauro Podda, Emanuele Botteri, Roberto Passera, Ferdinando Agresta, Alberto Arezzo. (2021)
- Forecasting Canadian GDP Growth with Machine Learning
Shafiullah Qureshi, Ba Chu, Fanny S. Demers. (2021)
- Morphological traits of reef corals predict extinction risk but not conservation status
Nussaïbah B. Raja, Andreas Lauchstedt, John M. Pandolfi, Sun W. Kim, Ann F. Budd, Wolfgang Kiessling. (2021)
- Machine Learning as a Tool for Improved Housing Price Prediction
Henrik I W. Wolstad and Didrik Dewan. (2020)
- Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates
Timothy J. Maguire, Scott O. C. Mundle. (2020)
- Predicting Risk of Delays in Postal Deliveries with Neural Networks and Gradient Boosting Machines
Matilda Söderholm. (2020)
Stock Market Analysis using Stacked Ensemble Learning Method
Malkar Takle. (2020)
- H2O AutoML: Scalable Automatic Machine Learning
. Erin LeDell, Sebastien Poirier. (2020)
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
Jin Sung Jang, Brian D. Juran, Kevin Y. Cunningham, Vinod K. Gupta, Young Min Son, Ju Dong Yang, Ahmad H. Ali, Elizabeth Ann L. Enninga, Jaeyun Sung & Konstantinos N. Lazaridis. (2020)
- Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML
Steven N. Hart, Eric C. Polley, Hermella Shimelis, Siddhartha Yadav, Fergus J. Couch. (2020)
- An Open Source AutoML Benchmark
Peter Gijsbers, Erin LeDell, Sebastien Poirier, Janek Thomas, Berndt Bischl, Joaquin Vanschoren. (2019)
- Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Sebastian Raschka, Joshua Patterson, Corey Nolet. (2019)
- Human actions recognition in video scenes from multiple camera viewpoints
Fernando Itano, Ricardo Pires, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandeza. (2019)
- Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm
Fernando Itano, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandez. (2018)
- askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men
Gregory B. Auffenberg, Khurshid R. Ghani, Shreyas Ramani, Etiowo Usoro, Brian Denton, Craig Rogers, Benjamin Stockton, David C. Miller, Karandeep Singh. (2018)
- Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs
Giorgio Alfredo Spedicato, Christophe Dutang, and Leonardo Petrini. (2018)
- Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
Yuriy Kochura, Sergii Stirenko, Yuri Gordienko. (2017)
- Algorithmic trading using deep neural networks on high frequency data
Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón. (2017)
- Generic online animal activity recognition on collar tags
Jacob W. Kamminga, Helena C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)
- Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning
Tomislav Hengl, Johan G. B. Leenaars, Keith D. Shepherd, Markus G. Walsh, Gerard B. M. Heuvelink, Tekalign Mamo, Helina Tilahun, Ezra Berkhout, Matthew Cooper, Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena. (2017)
- Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting
Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng, and Mark J. van der Laan. (2017)
- Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone. (2017)
- Using deep learning to predict the mortality of leukemia patients
Reena Shaw Muthalaly. (2017)
- Use of a machine learning framework to predict substance use disorder treatment success
Laura Acion, Diana Kelmansky, Mark van der Laan, Ethan Sahker, DeShauna Jones, Stephan Arnd. (2017)
- Ultra-wideband antenna-induced error prediction using deep learning on channel response data
Janis Tiemann, Johannes Pillmann, Christian Wietfeld. (2017)
- Inferring passenger types from commuter eigentravel matrices
Erika Fille T. Legara, Christopher P. Monterola. (2017)
- Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
Christopher Krauss, Xuan Anh Doa, Nicolas Huckb. (2016)
- Identifying IT purchases anomalies in the Brazilian government procurement system using deep learning
Silvio L. Domingos, Rommel N. Carvalho, Ricardo S. Carvalho, Guilherme N. Ramos. (2016)
- Predicting recovery of credit operations on a Brazilian bank
Rogério G. Lopes, Rommel N. Carvalho, Marcelo Ladeira, Ricardo S. Carvalho. (2016)
- Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering
Ebberth L. Paula, Marcelo Ladeira, Rommel N. Carvalho, Thiago Marzagão. (2016)
- Deep learning and association rule mining for predicting drug response in cancer
Konstantinos N. Vougas, Thomas Jackson, Alexander Polyzos, Michael Liontos, Elizabeth O. Johnson, Vassilis Georgoulias, Paul Townsend, Jiri Bartek, Vassilis G. Gorgoulis. (2016)
- The value of points of interest information in predicting cost-effective charging infrastructure locations
Stéphanie Florence Visser. (2016)
- Adaptive modelling of spatial diversification of soil classification units. Journal of Water and Land Development
Krzysztof Urbański, Stanisław Gruszczyńsk. (2016)
- Scalable ensemble learning and computationally efficient variance estimation
Erin LeDell. (2015)
- Superchords: decoding EEG signals in the millisecond range
Rogerio Normand, Hugo Alexandre Ferreira. (2015)
Understanding random forests: from theory to practice
Gilles Louppe. (2014)
Benchmarks
- Are categorical variables getting lost in your random forests?
Benchmark of categorical encoding schemes and the effect on tree based models (Scikit-learn vs H2O). Oct 28, 2016
- Deep learning in R
Benchmark of open source deep learning packages in R. Mar 7, 2016
Szilard's machine learning benchmark
Benchmarks of Random Forest, GBM, Deep Learning and GLM implementations in common open source ML frameworks. Jul 3, 2015
Presentations
- Pipelines for model deployment
Apr 25, 2017
- Machine learning with H2O.ai
Jan 23, 2017
Courses
- University of San Francisco (USF) Distributed Data System Class (MSDS 697)
Master of Science in Data Science Program
Masters of Applied Statistics Program
GWU: Data Mining (Decision Sciences 6279)
Masters of Science in Business Analytics
- University of Cape Town: Analytics Module
Postgraduate Honors Program in Statistical Sciences
- Coursera: How to Win a Data Science Competition: Learn from Top Kagglers
Advanced Machine Learning Specialization
Software
- modeltime.h2o R package
: Forecasting with H2O AutoML
Evaporate
: Run H2O models in the browser via Javascript. More info
: Splashing a User Interface onto H2O MOJO Files. More info
h2oparsnip R package
: Set of wrappers to bind h2o algorthms with the package
Forecast the US demand for electricity
: A real-time of the US electricity demand (forecast using H2O GLM)
h2o3-pam
: Partition Around Mediods (PAM) clustering algorithm in H2O-3
: Gap Statistic algorithm in H2O-3
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