Summer Schools (2017—2019)
| Foundations of Software Engineering |
| Skolkovo Institute of Science and Technology — Fall 2020, Fall 2021 | |
| This course is intended to serve as an introduction into basics of everyday industrial software engineering. Oftentimes students seek to obtain proficiency in complicated subjects such as machine learning, algorithms, or computer vision, but lack basic literacy in software engineering and therefore have little practical skills required to carry out research or industrial projects. In this course, our goal is to bridge the gap between basic programming skills commonly taught during BSc programs and the industrial-grade engineering required by top-notch MSc, Ph.D., or R&D positions. | |
| Materials 2020 Materials 2021 Videos 2020 Syllabus 2020 | |
| Geometric Computer Vision |
| Skolkovo Institute of Science and Technology — Spring 2021 | |
| Geometry plays an extremely important role in many computer vision algorithms as certain kinds of geometric transformations (e.g., projective) form the basis of imaging, estimation, and reconstruction. This course focuses on processing the geometry of 3D scenes and shapes, as obtained from both images and depth sensory data, using a series of learnable approaches. We will cover the standard geometry processing pipeline, study the depth acquisition systems, and dive into a variety of deep learning methods defined on semi-structured and unstructured geometric datatypes. Geometric learning-based systems differ from conventional ones by needing a custom way to construct low-level building blocks such as convolutional operations, that do not naturally exist for many geometric data structures. To this end, we will consider both familiar structures such as 2D images and 3D volumetric grids, and purely geometric ones such as point sets, meshes, implicit functions, and CAD representations such as parametric models. The course extensively leverages python programming skills focusing on numerical libraries such as numpy/scipy/pytorch, and requires basic knowledge of deep learning. Most of the software used within the course will be provided as docker images, thus knowledge of C++ or other tools should not be required. | |
| Materials 2021 Videos 2021 Syllabus 2021 | |
| Machine Learning in High Energy Physics Summer Schools (2017—2019) |
| Reading, UK; Oxford, UK; DESY, Hamburg, Germany — Summer 2017, Summer 2018, Summer 2019 | |
| The school is intended to cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including “Multivariate Analysis”, “Neural Networks”, “Classification/Clusterization techniques”. In more generic terms, these techniques belong to the field of “Machine Learning”, which is an area that is based on research performed in Statistics and has received a lot of attention from the Data Science community. | |
| Materials 2017 Materials 2018 Materials 2019 Course 2017 Course 2018 Course 2019 | |
| Machine Learning Summer School 2019 |
| Skoltech, Moscow, Russia — Summer 2019 | |
| Machine Learning Summer School (MLSS) is a 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. | |
| Materials 2019 Course 2019 | |
| Coursera AML: Deep Learning in Computer Vision |
| Higher School of Economics — 2018 | |
| This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings. | |
| Course 2018 | |
| Applied Statistics in Machine Learning |
| Higher School of Economics — Fall 2017, Fall 2018 | |
| This course provides an exploration of essential statistical concepts and methods crucial for data analysis across various scientific disciplines. We will start with the fundamental principles of resampling techniques such as Monte Carlo simulation and Bootstrap and how these methods underpin modern statistical inference. Next, we will cover parametric estimation methods, hypothesis testing, nonparametric estimation techniques, regression analysis, and the design of experiments. We will further explore advanced topics including distances between distributions, hypothesis testing methodologies like Neyman-Pearson lemma and A/B testing, as well as nonparametric criteria. Practical applications in fields such as neuroscience will be highlighted, demonstrating the relevance and applicability of statistical methods in real-world scenarios. By the end of the course, students will have acquired a robust foundation in statistical theory and methodology, enabling them to critically analyze data, make informed decisions, and contribute effectively to research and problem-solving endeavors in their respective fields. This course not only equips students with essential statistical tools but also fosters a deeper appreciation for the role of statistics in advancing scientific understanding and discovery. | |
| Materials 2018 | |
| Statistics of Random Processes |
| Higher School of Economics — Spring 2018 | |
| This course provides an introduction into stochastic processes, fundamental to understanding randomness and uncertainty in various systems. We start with the theoretical underpinnings of stochastic processes, including Wiener and Poisson type processes, Markov chains, and Markov processes. Practical aspects such as generating realizations of random processes will also be covered. Advanced topics will include Gaussian and conditional Gaussian models, Hidden Markov Models, and multivariate Gaussian estimation using techniques such as the Kalman filter. The course will also touch upon decision theory, introducing key statistics and tests crucial for making informed decisions in uncertain environments. More practical applications such as anomaly and change-point detection will be considered, demonstrating the relevance of stochastic processes in addressing real-world challenges across various domains. | |
| Materials ML 2018 Materials FinTech 2018 | |