• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Bachelor 2019/2020

Deep Learning

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'
Area of studies: Applied Mathematics and Information Science
Delivered by: Department of Informatics
When: 4 year, 1, 2 module
Mode of studies: offline
Language: English
ECTS credits: 4

Course Syllabus

Abstract

Discipline of choice. Deep learning is a popular area that uses neural networks of complex architecture. Such systems give better results in areas such as image and video processing, sound and text. The course will cover the main types of architectures and the principles of operation and training of deep neural networks and conduct practice in the above areas of application. As a result of mastering the discipline, the student must:  Know the methods of building deep neural networks;  Be able to apply deep learning to solve specific tasks;  own a mathematical apparatus and algorithms for working with deep neural networks.
Learning Objectives

Learning Objectives

  • The objectives of mastering the discipline "Deep Learning" are the formation of students' theoretical knowledge and practical skills on the basics of building large neural networks for deep learning.
Expected Learning Outcomes

Expected Learning Outcomes

  • Knows optimization algorithms for deep neural networks based on various variations of gradient descent. Configures such algorithms based on the conditions of a specific task
  • Knows the basic architecture of neural networks used to classify images. Modifies layers and various parameters to solve applied problems. It uses networks to solve the problems of image classification, image segmentation and video stream.
  • He knows the general, general scientific and business vocabulary used in the field of deep learning. It receives from articles (including in English) information about the structure of the neural network and the features used to solve a specific problem. Describes, presents and analyzes the results of applying deep learning methods to solve applied problems.
  • He knows the main types of tasks solved using deep learning. Develops architecture, implements, trains and optimizes the parameters of neural networks. It solves applied problems from various fields using deep learning.
Course Contents

Course Contents

  • Optimization and Regularization Algorithms
  • Image Processing and Analysis
  • Natural language processing, competitive and generative neural networks
  • Hyperparameter optimization, reinforcement learning
Assessment Elements

Assessment Elements

  • non-blocking Homework 1
  • non-blocking Homework 2
  • non-blocking Homework 3
  • blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * Exam + 0.16 * Homework 1 + 0.17 * Homework 2 + 0.17 * Homework 3
Bibliography

Bibliography

Recommended Core Bibliography

  • Iba, H. (2018). Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1833749

Recommended Additional Bibliography

  • Taweh Beysolow II. (2017). Introduction to Deep Learning Using R. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.1.4842.2734.3