Bringing the Leading Machine Learning Solution to Technical, Business and Social Science Degree Programs

Join us in Democratizing AI across any kind of degree program and shaping the future of higher education by becoming a member of the H2O.ai Academic Program. Program members have access to a wealth of resources including free non-commercial use of software licenses for education and research purposes; data sets and training materials; and collaboration with other members, makers at H2O.ai as well as partners and customers around the world. You can also collaborate with us at any of the H2O AI World conferences; co-host one of our popular meetups; or invite our highly skilled data science experts and Kaggle Grandmasters as a guest lecturer to one of your classes. The possibilities are endless, and the choice is yours!

H2O.ai provides impressively scalable implementations of many of the important machine learning tools in a user-friendly environment. Allowing for free academic use sets a generous example for commercial software developers — it is also the way forward in the era of open-source software.

Trevor J. Hastie John A. Overdeck Professor of Mathematical Sciences Professor of Statistics Professor of Biomedical Data Science Department of Statistics Stanford University USA

Using H2O.ai in our AI curriculum gives students a better understanding of machine learning techniques in a shorter period of time. Especially the ability to visualize and explain data inputs and explainable model outputs opens up machine learning to non-technical students in business degrees.

Michael Bliemel Ph.D M.M.S B.Sc Dean Professor of Management Information Systems Faculty of Business and Information Technology University of Ontario Institute of Technology Canada

I've been using H2O and autoML in my research on advertising effectiveness. Now with the Academic Program, we can use Driverless AI as an introduction to AI, and an illustration of modern enterprise use of AI for analyzing cross-sectional data, time-series data and NLP problems for teaching students: A dream come true!

Dr John Williams BCom, PGDipCom, MCom, DipGrad (Statistics), PhD Senior Lecturer Otaga Business School, Department of Marketing New Zealand
For Professors

Enrich Your Curriculum With Hands-on Data Science Labs

Whether you are teaching R and Python to scientists and engineers or want to enrich your non-technical degree programs with a machine learning hands-on lab, we have the right solutions for you: choose between our opensource H2O offering for the more technically included students, or level the playing field with our ground-breaking H2O Driverless AI for automatic machine learning.

Driverless AI enables a fully automatic machine learning workflow from data analysis, to automated predictions, and automatic model explanations. An ideal solution to teach AI/ML without the need for programming!

H2o In_Acedamic
For Students

Jump Start Your Data-science or Business Career

Machine learning is becoming the integral fabric in all of science and business. Hence, being well versed in this exciting and growing field is becoming the key requirement in many jobs and a key ingredient for a successful start into your career.

Don’t wait and apply for your personal membership in the H2O.ai Academic Program, the fastest and easiest way to gain this valuable, future proof skill. Even better, with the rapidly growing footprint of H2O.ai solutions across industries, you will open exciting internship opportunities with one of our customers or at H2O.ai. Don’t wait and get started now!

H2o In_Acedamic

Chat With Professors and Students about Data Science in Academia

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Share Knowledge

Swap tutorials and resources with your peers. Wrote a cool Driverless AI experiment? Share it here. Ask questions and get answers!

Build Together

Find people to work on projects with. Get feedback on your experiments and models.

Discuss Trends

Discuss where the field is going. Learn about the latest developments and how they're being used.

Meet the Slack Community Hosts

Discuss H2O, AI technologies and teaching them in the #academic slack channel
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Arno Candel

Rafael Coss

Rafael Coss


Eric Gudgion


Erin LeDell


Feng Bai


Gregory Kanevsky


Jo-Fai Chow


John Spooner


Jorge Hernandez Villapol


Karthik Kannappan


Lauren DiPerna


Leland Wilkinson


Manohar Vellala


Martin Barus


Megan Kurka


Nikhil Shekhar


Stefan Pacinda

Thomas ott

Thomas Ott


Tom Kraljevic


Venkatesh Yadav


Vinod Iyengar

H2O broadly used in Academia today

  • Courses
  • Research Papers
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.

IJCNN/WCCI 2018: Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm

Fernando Itano, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandez (2018)

Cognitive Systems Research 2019: Human actions recognition in video scenes from multiple camera viewpoints

Fernando Itano, Ricardo Pires, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandez (2019)

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)

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)

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)

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)

Superchords: decoding EEG signals in the millisecond range

Rogerio Normand, Hugo Alexandre Ferreira. (2015)

Sign Up

Please sign-up to the H2O.ai Academic Program with the form below. The global program is open to higher education institutions around the world as well as students currently enrolled in a higher education degree program; student status verification via student ID is required.

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