Специализация Advanced Machine Learning

Начинается Jul 16

Специализация Advanced Machine Learning

Deep Dive Into The Modern AI Techniques. You will teach computer to see, draw, read, talk, play games and solve industry problems.

Об этой специализации

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.

Автор:

Партнеры курса:

courses
7 courses

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projects
Проекты

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Сертификаты

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Обзор проектов

Курсы
Advanced Specialization.
Designed for those already in the industry.
  1. 1-Й КУРС

    Introduction to Deep Learning

    Upcoming session: Jul 16
    Выполнение
    6 weeks of study, 6-10 hours/week
    Субтитры
    English

    О курсе

    The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models.
  2. 2-Й КУРС

    How to Win a Data Science Competition: Learn from Top Kagglers

    Upcoming session: Jul 16
    Выполнение
    6-10 hours/week
    Субтитры
    English

    О курсе

    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 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.
  3. 3-Й КУРС

    Bayesian Methods for Machine Learning

    Upcoming session: Jul 16
    Выполнение
    6 weeks of study, 6 hours/week
    Субтитры
    English

    О курсе

    Bayesian methods are used in lots of fields: 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 really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks 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 fully 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 be found with Bayesian methods.
  4. 4-Й КУРС

    Practical Reinforcement Learning

    Upcoming session: Jul 23
    Выполнение
    6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section
    Субтитры
    English

    О курсе

    Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun!
  5. 5-Й КУРС

    Deep Learning in Computer Vision

    Upcoming session: Jul 16
    Выполнение
    5 weeks of study
    Субтитры
    English

    О курсе

    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 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 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 oftenly demonstrated in movies and TV-shows example of computer vision and AI.
  6. 6-Й КУРС

    Natural Language Processing

    Upcoming session: Jul 16
    Выполнение
    5 weeks of study, 4-5 hours per week
    Субтитры
    English

    О курсе

    This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.
  7. 7-Й КУРС

    Addressing Large Hadron Collider Challenges by Machine Learning

    Upcoming session: Jul 23
    Выполнение
    5 weeks of study
    Субтитры
    English

    О курсе

    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 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.

Авторы

  • National Research University Higher School of Economics

    Faculty of Computer Science (http://cs.hse.ru/en/) trains developers and researchers. The program was created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). It is also closely related to Yandex School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. In the faculty, learning is based on practice and projects.

    National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more. Learn more on www.hse.ru

  • Pavel Shvechikov

    Pavel Shvechikov

    Researcher at HSE and Sberbank AI Lab
  • Anna Kozlova

    Anna Kozlova

    Team Lead
  • Evgeny Sokolov

    Evgeny Sokolov

    Senior Lecturer
  • Alexey Artemov

    Alexey Artemov

    Senior Lecturer
  • Sergey Yudin

    Sergey Yudin

    Analyst-developer
  • Anton Konushin

    Anton Konushin

    Senior Lecturer
  • Ekaterina Lobacheva

    Ekaterina Lobacheva

    Senior Lecturer
  • Mikhail Hushchyn

    Mikhail Hushchyn

    Researcher at Laboratory for Methods of Big Data Analysis
  • Anna Potapenko

    Anna Potapenko

    Researcher
  • Nikita Kazeev

    Nikita Kazeev

    Researcher
  • Dmitry Ulyanov

    Dmitry Ulyanov

    Visiting lecturer
  • Marios Michailidis

    Marios Michailidis

    Research Data Scientist
  • Mikhail Trofimov

    Mikhail Trofimov

    Visiting lecturer
  • Andrei Ustyuzhanin

    Andrei Ustyuzhanin

    Head of Laboratory for Methods of Big Data Analysis
  • Alexey Zobnin

    Alexey Zobnin

    Accosiate professor
  • Alexander Guschin

    Alexander Guschin

    Visiting lecturer at HSE, Lecturer at MIPT
  • Dmitry Altukhov

    Dmitry Altukhov

    Visiting lecturer
  • Daniil Polykovskiy

    Daniil Polykovskiy

    Researcher
  • Alexander Novikov

    Alexander Novikov

    Researcher
  • Alexander Panin

    Alexander Panin

    Lecturer
  • Andrei Zimovnov

    Andrei Zimovnov

    Senior Lecturer

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