Introduction to neural network and machine translation

2020/2021

Учебный год

ENG

Обучение ведется на английском языке

8

Кредиты

Кто читает:

Кафедра прикладной математики и информатики (Нижний Новгород) (Факультет информатики, математики и компьютерных наук (Нижний Новгород))

Статус:

Курс по выбору

Когда читается:

4-й курс, 2, 3 модуль

Преподаватель

Дурандин Олег Владимирович

Course Syllabus

Abstract

The course introduces students to the basic concepts of data analysis and machine learning and the application of data mining and machine learning to solve practical problems in the professional field

Learning Objectives

• The purpose of learning is development of skills to conduct research, including problem analysis, setting goals and objectives, understanding of the object and subject of study, choice of method and research methods, as well as evaluating its quality.

Expected Learning Outcomes

• Has an idea of the basic concepts
• Has an idea about the regularization features
• Has an idea of principles of building neural networks
• Has an idea of the principles of neural networks training
• Has an idea of the features of convolutional networks
• Able to work with python ibraries

Course Contents

• The simplest methods of machine learning
  Applications of machine learning tasks. Classification problem. The K-NN method. Selection of model parameters by cross-validation. Linear classifier.
• The loss function. Regularization. Optimization.
  Logistic loss function. Quadratic, exponential and piecewise-linear loss function.
• Method of back propagation. Neural networks.
  Stochastic gradient descent. The simplest perceptron. Activation function.
• Training of neural networks. Normalization methods.
  Training of neural networks. Normalization methods.
• Convolutional neural network architectures.
  The concept of convolution. Pooling. Fully connected layer.
• Libraries for training and running neural networks.
  Libraries “Keras", "TenzorFlow".

Assessment Elements

• Control work
• Laboratory work 1
• Laboratory work 2
• Exam
  Итоговый контроль в 2019/2020 учебном году состоялся в 3 модуле.

Interim Assessment

• Interim assessment (3 module)
  0.3 * Control work + 0.4 * Exam + 0.15 * Laboratory work 1 + 0.15 * Laboratory work 2

Bibliography

Recommended Core Bibliography

• Kelleher, J. D. (2019). Deep Learning. Cambridge: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2234376

Recommended Additional Bibliography

• Антонио Джулли, Суджит Пал - Библиотека Keras – инструмент глубокого обучения. Реализация нейронных сетей с помощью библиотек Theano и TensorFlow - Издательство "ДМК Пресс" - 2018 - 294с. - ISBN: 978-5-97060-573-8 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/111438