Machine Learning is a powerful tool that is widely used today and is specified in computer science education, while Statistics and Probability are traditionally studied in mathematical departments. Nevertheless, there is a strong relationship between these theories, and it is desirable to be aware of both general principles and differences in approaches. Indeed, machine learning uses mathematical and/or statistical models to gain an overview of the data to make predictions. The novel contributions in data mining are mostly informal and usually linked with the Bayesian point of view on statistics. But, probability theory itself is more than just Kolmogorov's axiomatic approach or Bayesian reasoning. This course tries to show the existing diversity of approaches while staying within classical Kolmogorov's probability theory. The necessary classical theoretical material will be explained as well. We aim to consider several paradoxical situations that arise in practice. Most examples will be taken from natural sciences and simple situations in data analysis. The outcome is expected to be practical training in understanding the popular statistical models and the ability to critically read a professional text.

Deep Learning, i.e., training multilayered neural architectures, was one of the oldest tools in machine learning but has revolutionized the industry over the last decade. In this course, we begin with the fundamentals of deep learning and then proceed to modern architectures related to basic computer vision problems: image classification, object detection, segmentation, and others. Modern computer vision is almost entirely based on deep convolutional neural networks, so this is a natural fit that lets us explore interesting architectures, while at the same time staying focused and not going into too wide of a survey of the entire field of deep learning. Computer vision is also a key element in robotics: vision systems are necessary for navigation, localization and mapping, and scene understanding, which are all key problems for creating industrial and home robots.

Nowadays, machine learning technologies are widely used in various fields such as retail, mass media, PR and marketing, banking, telecommunications, manufacturing, science and many others. Even though it is very important to use the appropriate methods in every project, often the choice of a particular machine learning algorithm does not play a key role. Most of the time, the most important factors are the appropriate formulation of the problem from the business point of view, the correct mathematical formalization of the problem, and an accurate assessment of the potential economic effect. In the course, we will learn the structure and the lifecycle of the machine learning-based project and cover topics ranging from the problem statement definition to the final model quality assessment, as well as an estimation of the economic effect.

In three weeks, we're going to learn the principles of ML System Design and apply it to build an application that we and other people can use. This will include some amount of software development, product thinking, and machine learning. ML will be the backbone of our product, SE will be the means to make it work, and product thinking will lead us through. We’ll start with an idea, outline user scenarios an app should fulfil, proceed with system design, and dive into implementation. We’ll use the app ourselves, collect and analyse our feedback, work on improving the user experience, and finally publish this to the outside world to get real feedback and reflect on our experience. This class will help you understand how complex ML systems are built and will be part of your portfolio that you can showcase and reason about.

This three-week hands-on course provides a jumpstart into design and development of modern MLOps platforms. From initial project scoping to exploratory analysis and data engineering, from feature engineering to model training, to ML pipelines and CI/CD automation, productization, monitoring, and operations. You are going to create an ML project in Amazon SageMaker Studio and go through all stages of development such as data exploration, interactive experimentation, setting up MLOps pipelines, and finally deliver the project into production. You’ll learn how to work with a feature store, model registry, pipelines, model and data monitor, and CI/CD projects. Through this course, you will work with Amazon SageMaker to get you inspired to implement your own MLOps solution. The course provides recommended patterns and practical architecture blueprints for real-world ML projects. The course gives you an overview of the industry-standard ML platforms and MLOps products, like MLflow, Apache Airflow, H20, DataIKU. As the course's main deliverable, you’re required to implement an end-to-end ML project with the main components of MLOps, such as reproducible ML pipelines, scalable data processing and model training, model registry, experiment tracking, observability, and event-driven workflows.

During this course, students will master and sharpen their knowledge of basic technologies of the modern Big Data landscape, namely: HDFS, MapReduce, Hive, Spark (especially real-time instruments like Spark Streaming and Kafka Streams), and NoSQL frameworks like HBase and Cassandra. Of particular interest for the course will be the subject of efficient data warehousing using Hive and Spark. Under the teacher’s supervision, students will study the intricacies of the system’s internals and their applications. They will also learn distributed file systems, the purpose of their existence, and the ways to apply them. The learners will also practice using the MapReduce and Spark frameworks, a workhorse for many modern Big Data applications. The key element of this course is to apply knowledge into practice, and to process texts and solve sample business cases. The participants will deal with Spark, the next-generation computational framework, from its basic concepts up to advanced applications made to squeeze maximum performance. Finally, they will get acquainted with the modern NoSQL frameworks like HBase and Cassandra.

This course is about modeling real-world problems with optimization and numerical linear algebra tools and how to solve them. We will discuss the most important algorithms from NLA, classical convex optimization, and stochastic optimization methods which are crucial for efficient training of large neural networks. The selected topics will be discussed from both theoretical and practical perspectives. In particular, practice sessions will include introducing the standard open-source packages for solving considered problems. Every block of the course will be equipped with multiple practical demos and home assignments that help students go deeper into the discussed topics.

This course focuses on giving students the skills required to participate and excel in real-world ML tasks by practicing in ML competitions. The syllabus is designed to teach students how to solve ML problems going step-by-step. Students will create a framework for their solutions and test the problem by creating simple no-ML solutions, by preparing Python scripts, building Docker images, or deploying models. The course covers several topics, such as ML metrics understanding, optimization, EDA, feature generation, ensembling models, and debugging solutions.

Because the world is overwhelmed with information and people need automatic systems to filter and rank relevant information, recommendation systems are widespread technology today. We see them in video hosting services, social networks, online shops, etc. Recommendation systems are here to solve this task and save people’s time and effort on information retrieval. This course aims to introduce students to a full spectrum of recommendation systems tool belts, starting from simple user-based collaborative filtering to sequential recommendations based on transformers. Materials include both a theoretical part (matrix decompositions, SLIM) and a practical part addressing production techniques such as knn indexes, embedding quantization, and scalable item-to-item service.

In our data-driven world, the technological revolution has simplified the generation and collection of data across various aspects of our personal, social, and professional lives. The interpretation of this data empowers us to make informed decisions, generate ideas, recognize trends, and either validate or challenge our beliefs. This course aims to equip you with effective techniques for visualizing data, enabling you to comprehend and effectively communicate your findings to others, whether through a research paper, a corporate meeting, or a landing webpage. Additionally, we will cultivate a critical approach to data, enabling us to identify both its limitations and strengths.

Agentic AI Systems course examines the theory and engineering of autonomous agents: systems that perceive, plan, act, and adapt in dynamic environments. We will cover agent architectures, tool integration, memory and self-evaluation mechanisms, and decision-making under uncertainty. The course also emphasizes safety, robustness, and ethical considerations in agent design.

The course gives a 360° view of time series analysis methods and real-world problems. The main focus is on forecasting, with a range of solutions from simple averaging to NN-based sequential models. The course also covers unsupervised learning problems such as clustering and anomaly detection, as well as causal inference on time series data.

This course offers a comprehensive exploration of training, fine-tuning, and overseeing modern large-scale language models (LLMs) and generative models across various modalities such as text, images, sound, and video. Participants will acquire practical skills in utilising tools like Langchain and Huggingface for LLM development, deployment, and optimization. The curriculum also delves into computational resources for AI models, training multimodal language models, and enhancing computational efficiency. By the conclusion of the course, students will possess hands-on experience and knowledge of both open-source and proprietary models, culminating in a capstone project showcasing their proficiency in developing and applying these advanced AI models.

This course focuses on key and in-depth algorithms and data structures that form a modern computer specialist’s toolkit. The computational complexity of algorithms and their comparative analysis will also be discussed. Students will be extensively trained in implementing data structures and algorithms on many problems reducible to the discussed data structures and techniques. The programs will be tested against carefully prepared test cases using an automated testing system.

Clouds are an essential part of today’s life. Therefore, cloud computing skills are in high demand right now. The course curriculum includes the foundations of clouds: virtualisation and fundamental principles of distributed systems. In this module, different aspects of the design and implementation of distributed systems are explained. We will deep-dive into technologies like: google file system, spanner, dynamo DB, s3, and consensus algorithms like Raft and Paxos. The course also covers public clouds (AWS, GCP and Azure), and cost analysis of cloud solutions. Throughout the course, we will design and deploy cloud-native applications.

Cloud technologies have become increasingly prevalent in the business world, providing companies with the opportunity to deploy products and infrastructure with greater speed and scalability than ever before. However, clouds bring new security risks, threats, and tools that organizations must be prepared to tackle. Why is cloud security so different from “in-house” infrastructure and product security? How does cloud migration impact security risks? In this class, we get to know cloud technologies from a security point of view, and students will learn how to assess security risks and how to protect our products and infrastructure in clouds.

In this course, students will learn the basic principles of technological entrepreneurship. Students will be able to create a Minimal Viable Prototype (MVP) by applying principles of paper prototyping, create and implement a customer development pipeline and evaluate a product’s market fit and unit economics. They will will also learn how to create a pitch deck for their project from scratch, evaluate the quality of the early-stage venture capital, and implement a fund-raising plan. By the end of the course, students will have a good understanding of the overall properties of venture capital markets.

Creativity is the engine of the 21st century. The contemporary world is full of ideas, concepts, products, and people trying to find their way through this maze. Every start-up has to have not only the idea and (future) product but first of all the story to tell both to the potential investors as well as users, now and future, which brings us to the necessity of creativity and storytelling. Whatever you do, you have to have a story. Another intent of this course is bringing together people of different backgrounds and knowledge, to engage them in working towards a common goal in a new way – through writing a piece of fiction and performing it. Uniting those two fields gives students the possibility to try out their abilities in a safe environment as well as cooperate with other students, bringing in new viewpoints and experiences. This is one of the best ways of establishing teams that have innovation at their core.

This course is designed to equip participants with the necessary skills to cultivate long-lasting leadership influence and make a meaningful impact, even in the face of change, crises, and criticism. While technical or "hard" skills may secure high-potential individuals top positions, this course focuses on the development of "real skills" that enable leaders to build, sustain, and thrive in their leadership roles. The course will empower future leaders with the transformative Three Pillars of Leadership: Awareness, Behavior, and Visibility to take their leadership to the next level.

This course is a business English course for business people. It consists of authentic topics of great interest to everyone involved in or studying international business. The course reflects the latest trends in the business world. If you are into business, the course will greatly improve your ability to communicate in English in a wide range of situations. If you are a student of business, the course will develop the communication skills you need to succeed in a professional environment and will broaden your knowledge of the business world. Everybody studying this course will become more fluent and confident in using the language of business in a variety of contexts.

Pitching to Investors prepares students to craft and deliver concise, high-impact presentations to one of the most demanding audiences: investors. Over three weeks, participants refine their individual presentation style, learn how to frame startup narratives, and master the use of visual storytelling to support funding goals. The course culminates in a polished, investor-grade pitch.

Product Analytics Fundamentals is a comprehensive course that covers the essential elements of analytics, including applied statistics, programming skills, AB-testing, visualization, and understanding data. This course is designed to provide students with a solid foundation in analytics so they can make data-driven decisions. Students will learn how to collect and analyze data using statistical methods, how to extract insights from data, how to conduct AB-testing to optimize product performance, and how to visualize data to communicate insights effectively.

This course has two parts: (1) building and launching a user-facing software product with a special emphasis on understanding user needs, and (2) applying data-driven product development techniques to iteratively improve the product. Students will learn how to transform an idea into software requirements through user research, prototyping, and usability tests, and then they will proceed to launch the MVP version of the product. The students will apply an iterative, data-driven approach to developing a product, integrate event analytics, and run controlled experiments.

Building great tech requires more than great product knowledge and management - it requires a deep understanding of Agile ways of working. “Being agile” is so much more than dev team practices, it is being mentally flexible and highly adaptable to constant change. In this class, students will learn how to navigate the jargon and use agile concepts to achieve terrific results with teams and organizations. In addition to learning about the frameworks scrum and kanban, we will review the philosophical principles behind why these frameworks can be useful, how to modify or recombine them, and how to be sensitive to the human psychology that makes it all work. Through a combination of lectures, small-group work, learning games, workshops, and projects (both structured and self-directed), students will learn to avoid the pitfalls and leverage Agile for their product and business success.