Summer School of Machine Learning
Machine Learning School within Summer of Machine Learning at Skoltech (SMILES) — the intensive course about modern methods of statistical machine learning and inference. It presents topics which are at the core of modern machine learning, from fundamentals to state-of-the-art practice.
worldwide experts
Leading experts and world-recognized professionals in Machine Learning industry
hands-on training
Intensive hands-on tutorials, workshops and practice sessions related to deep learning, reinformcement learning, statistics and more.
Networking opportunities with relevant companies in the field, resulting in internships, employment and research options at the end of the course.
modern campus
Intensive 2 weeks experience with a full immersion into Skoltech's innovative and research-oriented community, as well as Russia's rich culture.
speakers & topics
Full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed as an IEEE Fellow.
Associate Professor in Department of Philosophy and an affiliated faculty member in the Machine Learning Department at Carnegie Mellon University. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery from various kinds of data and investigate learning problems including transfer learning, concept learning, and deep learning from a causal view. On the application side, he is interested in neuroscience, computer vision, computational finance, and climate analysis. He has published more than 100 papers on causality, machine learning, and artificial intelligence. He coauthors a best student paper for UAI and a best finalist paper for CVPR, and received the best benchmark award of the causality challenge, and has served as an area chair or senior program committee member for major conferences in machine learning or artificial intelligence, including NeurIPS, UAI, ICML, AISTATS, AAAI, and IJCAI. He has organized various academic activities to foster interdisciplinary research in causality
Assistant professor at Carnegie Mellon University appointed in both the Machine Learning Department and Tepper School of Business. His research spans core machine learning methods and their social impact and addresses diverse application areas, including clinical medicine and natural language processing. Current research focuses include robustness under distribution shift, breast cancer screening, the effective and equitable allocation of organs, and the intersection of causal thinking with messy data. He is the founder of the Approximately Correct (approximatelycorrect.com) blog and the creator of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. Find on Twitter (@zacharylipton) or GitHub (@zackchase).
Associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. He was a postdoctoral fellow at the University of Montreal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
Machine learning (ML) researcher with a focus on reinforcement learning (RL). MDPs and their generalizations (POMDPs, games) are my main modeling tools and I am interested in improving algorithms for solving them. In particular, I believe that finding the right ways to quantify uncertainty in complex deep RL models is one of the most promising approaches to improving sample-efficiency. I am also interested in meta learning and imitation learning. An important part of my research is motivated by applying RL to computer games.
Research scientist at Facebook AI Research and a full professor in the School of Computer Science at Tel-Aviv University, Israel. He conducted postdoctoral research at prof. Poggio's lab at the Massachusetts Institute of Technology and received his PhD degree from the Hebrew University, under the supervision of Prof. Shashua.
He is an ERC grantee and has won the ICCV 2001 and ICCV 2019 honorable mention, and the best paper awards at ECCV 2000 and ICANN 2016. His research focuses on computer vision and deep learning.
Dimitrios Pantazis, who joined the McGovern Institute in 2010, oversees the operation of the Magnetoencephalography (MEG) Laboratory within the Martinos Imaging Center at MIT. Before moving to MIT, he was research assistant professor at the University of Southern California from 2008-2010. His research focuses on the development of novel MEG methods to holistically capture spatiotemporal brain activation and the study of visual brain representations. He has over 15 years of experience in developing methods for the analysis of MEG data and has published prominent articles in Nature Neuroscience, Proceedings of the National Academy of Sciences, Scientific Reports, Cerebral Cortex, and NeuroImage. His work has been featured in Science News; Scientific American Mind Magazine; Boston Magazine; MIT Technology Review; Elekta's Wavelength Magazine; MIT News; the front-page of the MIT website and several Greek media outlets.
Interested in developing flexible, interpretable, and scalable machine learning models, often involving deep learning, Gaussian processes, and kernel learning. I care about developing practically impactful methods, while at the same understanding why the methods work, and the foundations for building models that learn and generalize. I am particularly excited about loss surfaces, generalization, probabilistic generative models, and Bayesian methods in deep learning. My work has been applied to time series, vision, NLP, spatial statistics, public policy, medicine, and physics
event details
SMILES School will take place at the heart of Moscow's Skolkovo Innovation Center – Skoltech Institute of Science and Technology, rated globally as one of TOP-100 young universities (by Nature Index). Skoltech was created during 2011 in collaboration with the Massachusetts Institute of Technology (MIT). Central to the campus are its multi-story laboratories that accommodate lab space, work and teaching areas, as well as the associated offices, which include 12 laboratory buildings within the insitute.
Moscow is the capital of Russia and one of the largest cities in Europe. A historic center with modern infrastructure easily reachable from all major cities of Russia by air or train travel. Located on the river Moskva, in the west of the country, Moscow's landmarks include the Red Square, the Bolshoi Theatre, the Gorky Park, VDNH and the Tretyakov gallery. Moscow is an excellent destination to visit regardless whether you are on business or holiday travel.
fees, travel, accommodation
RUB 8000
for students (PhD as well as strong MSс applicants) from Russia, nearby and Russian-speaking countries
RUB 18000
for students from all other regions (PhD as well as strong MSc applicants)
RUB 22000
for industry members and practitioners
RUB 27000
corporate packs can be offered at a special price — contact us to arrange it
Namely: Russia, Belarus, Ukraine, Moldova, Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan, Turkmenia, Armenia, Azerbaijan, Georgia
what's included
participation in all courses, daily lunches & coffee breaks, Moscow tour, two social events, community day and shuttle to the venue
discounted twin rooms, paired with another participant, and single rooms are available. A shuttle commuting daily between hotel and venue will be offered.
For application, you will need the items listed below.
CV (max 2 pages including education, work experience and other relevant info)
Academic records
One recommendation letter (max 500 words)*
Motivation statement
(max 500 words)
Title and abstract
of your poster*
* Optional for industry applicants
Who can apply?
Early and mid stage PhD students. However, we will also consider strong applications by MSc students subject to space and availability. We also have a separate application category for postdocs, faculty and industry practitioners. Industrial applicants can also obtain tickets via industry sponsorship.
When can I apply?
The application system is open until the 17th of May 2020. See the application page for more information on the process, and other important dates.
We review all applications and send invitations to those who are selected in the middle of June
How do I apply?
Please see the Application Guidelines section
What is the poster session?
Posters are a long-standing tradition at the MLSS. Each applicant is asked to bring along a poster presentation covering some of their own work. While the day program covers broad areas of general interest, the poster sessions offer participants a chance to discuss their own work with their peers. The poster presentations will be a relaxed affair: Posters will be distributed across a room, with participants presenting to small groups of two or three standing in front of their posters.
Does my poster need to be new work?
Posters can be both existing and unpublished work (ongoing work, or work resulting from undergraduate projects is also fine, were applicable).
Are there any Travel Grants or Scholarships for attending the Summer School?
Full and partial (up to 50%) stipends are available to students with a strong profile.
Recipients will be informed of the details in their confirmation mail.
Please, specify that you need financial support in the registration form.
I am not affiliated with any university or company currently. Can I still apply?
Please write "None" in the affiliation field and provide additional details in your motivation letter. You will still need to provide a letter of reference.
I live in Moscow, can I visit the school?
Yes, sure. But registration is required for all participants anyway.
organizers / contact
Alexander Kuleshov
Co-Chair, President, Skoltech
Evgeny Burnaev
Co-Chair, Associate Professor,
CDISE, Skoltech
Maxim Fedorov
Co-Chair, Director, CDISE, Skoltech
Rodrigo Rivera Castro
Researcher, CDISE, Skoltech
Ivan Khlebnikov
SMILES 2020 fundraising director
Skolkovo Institute of Science & Technology
Center for Computational and Data-Intensive Science and Engineering


Do not hesitate to drop by:
30с1 Bolshoi boulevard, Skolkovo, 121205, Russian Federation
For directions on how to reach us, please see here