Education
Universities | Courses | Learning materials | Summer schools
Universities
- Masters in Applied Statistics and Data Science @ Yerevan State University and ISTC: math.ysu.am/asds
- Machine Learning Masters @ Russian-Armenian University:
imi.rau.am/program/master (website under construction as of Dec 2020)
- Main contact: Arman Darbinyan
- Bachelors in Data Science @ American University of Armenia: cse.aua.am/ds
- Masters in Data Science in Business @ Yerevan State University and ISTC: armdsforb.wordpress.com
Courses
Industry and other institutions also offer private and public courses in machine learning. Please check their websites for current courses and enrollment deadlines.
Learning Materials
YerevaNN - A Guide to Deep Learning (2016)
yerevann.com/a-guide-to-deep-learning/ (last updated: December 2016)
RAU NLP
Russian-Armenian University
Machine Learning Masters
Natural Language Processing course
rau-nlp.github.io
ISTC - YSU Mathematics Summer School
Machine Learning for Natural Language Processing
deeplanguageclass.github.io
Seminars on PAC-Bayesian generalization bounds
Seminar series page
ML EVN
r/MLEVN flair:education
Summer Schools
(ordered by application deadline)
- Dates: July 7 - July 15, 2021
- Application deadline: March 31
- Covers deep learning and reinforcement learning
- Aaron Courville, Doina Precup are among the speakers
- Organized by DeepMind
- Location: Virtual
- Application deadline: April 9
- Co-organized by CIFAR, Alberta Machine Intelligence Institute, MILA and Vector Institute
- Location: Virtual
- Dates: August 9 – August 20, 2021
- Application deadline: April 30
- Covers key ML topics (Bayesian ML, Computer Vision, NLP and reinforcement learning, Causal ML, Topological ML), with a special focus on medicine (medical imaging, genetics, drug discovery, and learning from multi-modal medical data).
- Organised by AI for Global Goals and in partnership with Oxford Saïd Business School and CIFAR.
- Location: Virtual
- Dates: July 7 - July 15, 2021
- Application deadline: May 15
- Covers both basic and advanced topics in ML
- Location: Virtual
Past