Neural Networks and Deep Learning

Bachelor 2020/2021

Type: Elective course (Business Informatics)

Area of studies: Business Informatics

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 4 year, 2 module

Mode of studies: distance learning

Instructors: Andrey Dmitriev

Language: English

ECTS credits: 4

Contact hours: 2

Full Syllabus Ask Question

Abstract

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new &amp;quot;superpower&amp;quot; that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning#syllabus

Learning Objectives

In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture

Expected Learning Outcomes

Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

Course Contents

Introduction to deep learning
Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
Neural Networks Basics
Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
Deep Neural Networks
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

Assessment Elements

Практическое упражнение к теме №1, выполненное в ходе изучения онлайн курса на платформе Coursera
Практическое упражнение к теме №2, выполненное при прохождении онлайн курса на платформе Coursera
Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera
Практическое упражнение к теме №4, выполненное при прохождении онлайн курса на платформе Coursera
Экзамен
Перезачет оценки, полученной по итогам прохождения онлайн курса на платформе Coursera. Перевод оценки в итоговую оценку в десятибалльной шкале производится путем деления оценки, полученной по итогам прохождения онлайн курса на платформе Coursera, на 10 и округления полученного результата по правилам математического округления

Interim Assessment

Interim assessment (2 module)
0.247 * Практическое упражнение к теме №1, выполненное в ходе изучения онлайн курса на платформе Coursera + 0.247 * Практическое упражнение к теме №2, выполненное при прохождении онлайн курса на платформе Coursera + 0.248 * Практическое упражнение к теме №3, выполненное при прохождении онлайн курса на платформе Coursera + 0.248 * Практическое упражнение к теме №4, выполненное при прохождении онлайн курса на платформе Coursera + 0.01 * Экзамен

Bibliography

Recommended Core Bibliography

Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight Uncertainty in Neural Networks. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1505.05424
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
Kim, P. (2017). MATLAB Deep Learning : With Machine Learning, Neural Networks and Artificial Intelligence. [New York, NY]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1535764
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386

Recommended Additional Bibliography

Alexander, D. (2020). Neural Networks: History and Applications. Nova.
Graves, A., Fernàndez, S., Gomez, F., & Schmidhuber, J. (2017). Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.163BBE7B
Nika Sajko, Simon Kovacic, Mirko Ficko, Iztok Palcic, & Simon Klancik. (2020). Manufacturing Lead Time Prediction for Extrusion Tools With the Use of Neural Networks. Management and Production Engineering Review, 11(3), 48–55. https://doi.org/10.24425/mper.2020.134931
Paweł Kaczmarczyk. (2020). Feedforward Neural Networks and the Forecasting of Multi-Sectional Demand for Telecom Services : a Comparative Study of Effectiveness for Hourly Data. Acta Scientiarum Polonorum. Oeconomia, 8(3), 13–25. https://doi.org/10.22630/ASPE.2020.19.3.24

Course Syllabus