Machine Learning - H2O
Blog Posts & Tutorials
21_ENTRIES- 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
10_ENTRIES- 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
43_ENTRIES- 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 predict…
Padmavati Kulkarnia, V.Sreekantha, Adithi R.Upadhyab, Hrishikesh ChandraGautama. (2022)
- Prospective validation of a transcriptomic severity classifier among patients with suspected acute…
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)
- Machine Learning-based Meal Detection Using Continuous Glucose Monitoring on Healthy Participants: …
Victor Palacios, Diane Myung-kyung Woodbridge, Jean L. Fry. (2021)
- Maturity of gray matter structures and white matter connectomes, and their relationship with psychi…
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 …
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 prima…
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 learni…
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…
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 Activ…
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 reso…
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 an…
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 …
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 learni…
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 …
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 lo…
Stéphanie Florence Visser. (2016)
- Adaptive modelling of spatial diversification of soil classification units. Journal of Water and La…
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
3_ENTRIES- 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
2_ENTRIES- Pipelines for model deployment
Apr 25, 2017
- Machine learning with H2O.ai
Jan 23, 2017
Courses
6_ENTRIES- University of San Francisco (USF) Distributed Data System Class (MSDS 697)
Master of Science in Data Science Program.
- UCLA: Tools in Data Science (STATS 418)
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
8_ENTRIES- modeltime.h2o R package
Forecasting with H2O AutoML
- splash R package
Splashing a User Interface onto H2O MOJO Files. More info here.
- h2oparsnip R package
Set of wrappers to bind h2o algorthms with the parsnip package.
- Forecast the US demand for electricity
A real-time dashboard of the US electricity demand (forecast using H2O GLM)
- h2o3-pam
Partition Around Mediods (PAM) clustering algorithm in H2O-3
- h2o3-gapstat
Gap Statistic algorithm in H2O-3