Spécialisation Advanced Machine Learning

Commence le May 21

Spécialisation 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.

À propos de cette Spécialisation

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.

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courses
7 courses

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projects
Projets

Conçu pour vous aider à vous exercer et à appliquer les compétences que vous avez acquises.

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Certificats

Mettez en évidence vos nouvelles compétences sur votre CV ou sur LinkedIn.

Vue d'ensemble des projets

Cours
Advanced Specialization.
Designed for those already in the industry.
  1. COURS 1

    Introduction to Deep Learning

    Session à venir : May 21
    Engagement
    6 weeks of study, 6-10 hours/week
    Sous-titres
    English

    À propos du cours

    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 st
  2. COURS 2

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

    Session à venir : May 21
    Engagement
    6-10 hours/week
    Sous-titres
    English

    À propos du cours

    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
  3. COURS 3

    Bayesian Methods for Machine Learning

    Session à venir : May 21
    Engagement
    6 weeks of study, 6 hours/week
    Sous-titres
    English

    À propos du cours

    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 all
  4. COURS 4

    Practical Reinforcement Learning

    Session à venir : May 28
    Engagement
    6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section
    Sous-titres
    English

    À propos du cours

    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 t
  5. COURS 5

    Deep Learning in Computer Vision

    Session à venir : May 21
    Engagement
    5 weeks of study
    Sous-titres
    English

    À propos du cours

    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
  6. COURS 6

    Natural Language Processing

    Session à venir : May 21
    Engagement
    5 weeks of study, 4-5 hours per week
    Sous-titres
    English

    À propos du cours

    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-d
  7. COURS 7

    Addressing Large Hadron Collider Challenges by Machine Learning

    Commence le May 2018
    Engagement
    5 weeks of study
    Sous-titres
    English

    À propos du cours

    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.

Créateurs

  • Université nationale de recherche, École des hautes études en sciences économiques

    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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