Программы онлайн-образования

бакалавриата

магистратуры

дополнительного и бизнес-образования

довузовской подготовки

онлайн-образования

НИУ ВШЭ

The course is devoted to the usage of computer vision libraries like OpenCV in 2d image processing. The course includes sections of image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, video tracking. Aim of the course: • Learning the main algorithms of traditional image processing • Thorough understanding of benefits and limitations of traditional image processing Practical Learning Outcomes expected: • Mastering programming skills of image processing with computer vision libraries

The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this online course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.

You will master your skills by solving a wide variety of real-world problems like image captioning and automatic game playing throughout the course projects. You will gain the hands-on experience of applying advanced machine learning techniques that provide the foundation to the current state-of-the art in AI

During the course "Mathematics for ComputerVision", students will complete the project “Use matrix filters and set functions to detect the edges of an object in the image”. During the working at this project, students using Python programming language (Jupiter's notebook), will implement the application that performs the required functions. All operations will have to be implemented independently without using third-party libraries. At the course "Object-oriented Programming", students will apply the acquired skills and develop an application in C++ programming language. At the course "2D Image Processing", students will design the application -Development of C++ application that use OpenCV to detect and count coins in a given image. In addition, they will test the quality of created detector application

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money. In this online HSE course we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases can be found with Bayesian methods.

This course is designed to open the doors of the world of business analytics. Nowadays a lot of organizations make their decisions based on data-driven approach. How to make the right decision? Which methods are used in multinational companies? This course is about demonstrating the diversity of real cases and applications of methods, techniques, and theories in various areas. Each week of this course is a piece of a puzzle where you will meet different experts from the industry who will share with you best practices from the market. Bringing together all the pieces you will understand the key definitions used in business analytics and will learn about data analytics techniques which can be applied in marketing, sales, PR, HR, and finance. “Business Analytics: Diversity of Practical Applications” aims to help you to navigate in the variety of career opportunities which are opened for business analysts.

Our online course aims to provide necessary background in Calculus sufficient for up-following Data Science courses. Course starts with a basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning. Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes. Трудоемкость:3 ЗЕ. 12 заданий с автоматическим оцениванием

Despite a large variety of different courses on analytics, the courses that offer a broad overview of the field are rare. From practice of teaching statistics, it became clear that it is difficult for learners to put together a broad field map if they have taken only a few of the different topics on analytical tools. As a result, they do not see the overall picture of everything that the field of data analysis has to offer. This course is designed to fill this gap. It is a survey course on state-of-the-art in interdisciplinary methods of data analysis, applicable to business and academia alike. Unlike other statistical courses, which focus on specific methods, this course will focus on the broader areas within statistics and data analytics. There are five major topics it will cover. It will start with the root of it all - the data – and some of the problems with the data. Then it will move through the contemporary approaches to descriptive, inferential, predictive and prescriptive analytics. Within each broader topic, the course will offer the theoretical foundation behind the methods without focusing too much on the mathematics. It will provide historical references, examples, explanations and case studies to illustrate the main concepts within each broader topic. In doing so, it will introduce the applied, problem-based approach to using specific tools. Then, it will discuss some of the specific of a particular approach. Overall, after taking this course, the students will get a good understanding of the state-of-the-art tools that the field of data analysis currently has to offer. The course consists of two parts. There is a review part with six lectures, providing the description of the major data analysis areas. This 6-lecture course is offered as part of the “Network analytics for business” specialization. For students of the “Master of data and network analytics” program, there are six additional lectures on specific topics. They are designed to illustrate some of the specific state-of-the-art approaches within the broader areas.

Corporate Finance course develops a conceptual framework to analyze the broad area of corporate financial decisions (investing, financing, payout, strategic deals). It provides the prevailing empirical evidence for the motives and impact of corporate performance in developed and emerging capital markets. The online course covers topics in corporate financial architecture (e.g. governance issues, board’s role), ownership structure, and corporate performance of a firm. Corporate Finance course develops a conceptual framework to analyze the broad area of corporate financial decisions (investing, financing, payout, strategic deals). It provides the prevailing empirical evidence for the motives and impact of corporate performance in developed and emerging capital markets. The online course covers topics in corporate financial architecture (e.g. governance issues, board’s role), ownership structure, and corporate performance of a firm. During the Corporate Finance course, you will gain insight into both theoretical and practical aspects of Corporate Finance, including financing and capital structure of organization, its life-cycle stage and payout policy decisions. You will learn about the dilemmas of executives and shareholders and such recent trends as knowledge firms and zero debt capital structure.

This specialization gives an introduction to reinforcement learning, natural language understanding, computer vision. Upon completion of 4 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this online course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In the course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and often demonstrated in movies and TV-shows example of computer vision and AI.

The online course in Digital Literacy offered by HSE University was designed to help the students acquire various competencies that will enable them to safely and effectively use digital technologies and Internet resources in an academic and professional context. The Digital Literacy course is a step-by-step guide covering various topics that will help you find your way around in the digital environment. We did our best to include the most essential insights and skills that can guarantee your success in the world of information technology. And we packed them all into one course. This course highlights some aspects that may seem simple but far from self-evident, like the parameters to keep in mind when looking for a new computer, the right ways to prevent online data theft, the inner workings of the Internet, and the laws that govern online activities. It also covers the basics of handling data and provides essential skills required for working with information sources, customizing table design, and doing simple analytics. You will find this extremely helpful not only for your time spent at the university but also at your future job. To take part in the course, you will need a computer with Internet access and some free memory to install the required software, a Google account, and a pre-installed MS Office package.

The main goal of this online course is to introduce topics in Discrete Mathematics relevant to Data Analysis. We will start with a brief introduction to combinatorics, the branch of mathematics that studies how to count. Basics of this topic are critical for anyone working in Data Analysis or Computer Science. We will illustrate new knowledge, for example, by counting the number of features in data or by estimating the time required for a Python program to run. Next, we will apply our knowledge in combinatorics to study basic Probability Theory. Probability is everywhere in Data Analysis and we will study it in much more details later. Our goals for probability section in this course will be to give initial flavor of this field. Finally, we will study the combinatorial structure that is the most relevant for Data Analysis, namely graphs. Graphs can be found everywhere around us and we will provide you with numerous examples. We will mainly concentrate in this course on the graphs of social networks. We will provide you with relevant notions from the graph theory, illustrate them on the graphs of social networks and will study their basic properties. In the end of the course we will have a project related to social network graphs. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in Python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in Data Analysis, starting from motivated high school students. Пререквизиты: знание английского языка

The course builds essential skills necessary for economic, business or financial analysis. The purpose of the course is to give students solid and extended skills in both econometric tools and their application to contemporary economic problems. We will learn both theoretical foundations and practical aspects of the main econometric topics: ordinary least squares as a core approach of linear regression analysis, the choice of the model specification, dealing with main problems of econometric analysis such as multicollinearity, heteroscedasticity, autocorrelation and endogeneity. After the course, you will be able to perform your own economic data analysis based on the understanding of described econometrics tools. You will learn how to apply the indicated tools and methods to various topics of your research and how to prevent and overcome problems, which can arise in real data analysis

Financial markets and Instruments course is focused on the features and the use of various types of financial instruments including stocks, bonds and derivatives. It covers financial intermediation, market infrastructure and regulation. The emphasis is made on general economic intuition rather than on quantitative models. The course syllabus corresponds to the majority of learning outcome statements of CFA levels 2 and 3 and can be used a preparation resource for CFA certification. In comparison to other courses of this topic, this course covers such financial institutions as rating agencies, pension funds, hedge funds, venture capital and private equity funds, sovereign wealth funds). The course is a major professional core course of the online degree Master program. Financial Markets and Instruments is the basic course, which is required for the further study of asset pricing using various quantitative models. These topics are covered in the Theory of Finance. The main objective of the course is to provide detailed and thorough understanding of how financial markets work. Having successfully completed the course, students should be able to explain the differences between various types of financial instruments, understand the role of financial institutions in different types of financial markets. The course is also aimed to develop analytical and practical skills in the area of financial markets.

Long-term projects require a thorough analysis, negotiations with investors, lenders, and partners. The financial model in MS Excel is the most important tool for planning, structuring, and analysis of a project. Here you pull up and summarize all available information on your expectations and forecast. Quality of your model defines the precision of your decisions and your ability to convince the other participants of the project. In this online-course you will learn how to build for your project a financial model with all reports and ratios that used in project finance. We will go through the path from a blank Excel workbook, discuss the best practice for financial modeling, dig into technical and financial aspects of the methods and ratios that we use in the model. Within the course we will work with a typical project finance model, but this model has a wide range of possible applications. The same structure and principles could be used for any project to create a new business, launch a product or for venture investments. You will see that the approach we use is universal and the model we build in the course can be your template for many future projects. The course is aimed at students with some experience in Excel. We will not be learning the basics of spreadsheets or MS Excel interface, but all special functions and services will be presented and explained before we use them in our model. All stages the development of the model will be supplemented with Excel files, so by the end of the course you will have both new skills and ready professional financial models. During the last week of the course you will develop your capstone project – your own financial model. After the HSE course, students will have necessary knowledge and skills to build a fully-fledged financial model for a project or a company. The model can be used to support investment decisions, raise capital or apply for debt financing The course consists of short video lectures, 7 to 12 minutes long, with embedded non-graded questions. Each week there will be a graded test, a and a final exam – capstone project. Students will choose a real or fictious project, collect necessary data and build a model from a template provided here. Finally, you will be required to submit your model for peer evaluation. It gives them a taste of what financial analysts go through in real life when developing a financial model. The goal of the course is to provide practical skills in financial modelling of investment projects by discussing best practice with the teacher as well as students work on their own projects. Students’ performance is evaluated on a 10-point mark scale as follows: 10 points - Distinguished performance 8-9 points - Excellent performance 6-7 points - Good performance 4-5 points - Satisfactory performance 0-3 points - Fail The rounding of the definitive performance grade is conducted in accordance with the standard mathematical rounding rules. The rounding of the intermediate grades is not conducted to avoid the rounding bias. The instructors use traditional methods of instruction by providing well-structured reading during contact hours with a lot of illustrations, problems and real case studies and discussing the materials.

The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability. This online course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well. Пререквизиты: знание английского языка

If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science. In this course, you will learn to analyse and solve competitively such predictive modelling tasks. When you finish this class, you will: - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks. - Learn how to preprocess the data and generate new features from various sources such as text and images. - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them. Disclaimer : This is not a machine learning online course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.

Industrial Organization is the area of economics that studies the markets as institutions, the state of competition and strategic interaction among firms, the industrial policy and the business decisions firms make within the market framework. The course looks at the markets from three different perspectives: the economic theory, the applied business perspective and the institutional and legal perspective. The focus of the course is split equally between the economic theory and business perspective but there is a significant legal component incorporated in various topics. The course includes economic modeling, game theory, numerous real life examples and several case studies. We explore interesting topics of market organization such as negotiations, antitrust, networks, platforms, electronic markets, intellectual property, business strategies, predation, entry deterrence and many others. The basic objective of the course is to enable the student to understand the structure of markets and the nature of strategic competition. Knowledge in this course will be valuable for the students in acquiring managing and governance skills, enriching their understanding of the institutional framework of business, and improve their analytical ability in negotiations. Prerequisites: The course requires understanding of basic economic modeling, knowledge of intermediate microeconomics (especially production/cost theory), knowledge of basic concepts and methodologies of game theory, intermediate econometrics and basic calculus. Трудоемксоть: 2 ЗЕ. 10 заданий с автоматическим оцениванием Пререквизиты: знание английского языка

This course will provide students with knowledge on the formation and adoption of the international financial reporting standards, their basic concepts, and the requirements for the preparation and content of the financial statements used in the international practice. It will also prepare them for practical issues on financial reporting.