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Аспирантура 2023/2024

Методы машинного обучения

Статус: Курс по выбору
Направление: 00.00.00. Аспирантура
Когда читается: 1-й курс, 1 семестр
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Преподаватели: Кертес-Фаркаш Аттила
Язык: английский
Кредиты: 4
Контактные часы: 36

Course Syllabus

Abstract

This course gives an introduction to the most popular discriminative and differentiable machine learning methods, which are used in supervised learning.
Learning Objectives

Learning Objectives

  • The student should be able to design and implement a basic machine learning system
Expected Learning Outcomes

Expected Learning Outcomes

  • Students know about the basic definitions of machine learning, and the evaluation of their performance.
  • Student knows about basic linear and non-linear classifiers, decision trees, genetic algorithms.
  • The student is familiar with the application independent, theoretical information distance
  • The student becomes familiar with the implementation of deep neural networks. The students gain hands-on experience with implementing deep neural networks in python for real-world applications.
  • The student becomes familiar with the implementation of recurrent, sequential deep neural networks. The students also gain hands-on experience with implementation of deep neural networks in python for real-world applications with sequential data, such as neural machine translation.
  • The student becomes familiar general differentiable architectures
  • The student learns about these basic concepts of machine learning.
Course Contents

Course Contents

  • Basic methods
  • Introduction to machine learning, Evaluation techniques
  • Distance functions
  • Deep Neural Networks
  • Methods for sequential data
  • Neural Turing Machines
  • Algorithm independent machine learning
Assessment Elements

Assessment Elements

  • non-blocking Экзамен
  • non-blocking Посещение лекций
Interim Assessment

Interim Assessment

  • 2023/2024 1st semester
    0.3 * Посещение лекций + 0.7 * Экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • Deep learning, Goodfellow, I., 2016

Recommended Additional Bibliography

  • Pattern recognition and machine learning, Bishop, C. M., 2006

Authors

  • KERTES-FARKASH ATTILA -
  • Антропова Лариса Ивановна