Bachelor 2019/2020

Introduction to neural network and machine translation

Category 'Best Course for Career Development'

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Elective course (Fundamental and Applied Linguistics)

Area of studies: Fundamental and Applied Linguistics

Delivered by: Department of Applied Mathematics and Informatics

Where: Faculty of Humanities (Nizhny Novgorod)

When: 4 year, 2, 3 module

Mode of studies: offline

Instructors: Oleg Durandin

Language: English

ECTS credits: 6

Contact hours: 62

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