Кафедра прикладной математики и информатики (Нижний Новгород)

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

Introduction to neural network and machine translation

Преподаватель

Course Syllabus

Abstract