Кафедра прикладной математики и информатики (Нижний Новгород)

АБB
АБB
АБB

Обычная версия сайта

Публикации

Книга

Теория вероятностей и математическая статистика

Колданов А. П., Колданов П. А.

М.: Издательский дом НИУ ВШЭ, 2023.

Статья

Применение нейронных сетей для прогнозирования прочности древесно-минерального композита

Долматов С. Н., Бабкина Т. С.

Системы. Методы. Технологии. 2023. № 1(57). С. 87-94.

Глава в книге

Application of an Adaptive Domain-Specific Interface in a Decision-Support System for Planning Railroad Technical Services

Ulitin, B., Babkin, E., Babkina, T.

In bk.: Model-Driven Organizational and Business Agility: Second International Workshop, MOBA 2022, Leuven, Belgium, June 6–7, 2022, Revised Selected Papers. Iss. 457: Model-Driven Organizational and Business Agility. MOBA 2022. Switzerland: Springer, 2022. P. 110-124.

Препринт

On the multi-stage shortest path problem under distributional uncertainty (submitted to Journal of Optimization Theory and Applications)
В печати

Sergey S. Ketkov.

arXiv "math". 2205.09200. Cornell University, 2022

Все публикации

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

Статус:: Маго-лего

Когда читается:: 2 модуль

Преподаватель

Гречихин Иван Сергеевич

Full Syllabus

Abstract

The course is devoted to the presentation of modern data analysis and machine learning methods that are widely used in computer vision. The main emphasis is placed on such sections as learning and inference in vision. A taxonomy of models that relate the measured image data and the actual scene content is studied. Generative and discriminative models Classification, regression and clustering methods.

Learning Objectives

Mastering theoretical background of machine learning.
Obtaining skills of the correct selection of methods for solving the problem

Expected Learning Outcomes

Distinguish the main tasks of machine learning
Process tabular data
• Train and validate linear models for classification and regression problems
• Apply regularization to address overfitting
• Train and validate decision tree models for classification and regression problems
• Distinguish components of learning error
• Train and validate KNN model
• Apply dimensionality reduction to data
• Perform cluster analysis
• Train MLP for supervised learning taks

Course Contents

Introduction to Machine Learning
Linear Models
Decision Trees
Ensemble Learning
Unsupervised Learning
Introduction to Artificial Neural Networks
Final Task with instructor’s evaluation

Assessment Elements

Programming Assignment
Tests
Final exam

Interim Assessment

2022/2023 2nd module
0.4 * Final exam + 0.2 * Tests + 0.4 * Programming Assignment

Bibliography

Recommended Core Bibliography

Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
Computer vision : models, learning, and inference, Prince, S. J. D., 2012
Introduction to machine learning, Alpaydin, E., 2020
Machine learning : a probabilistic perspective, Murphy, K. P., 2012
McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.
Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)

Recommended Additional Bibliography

Core data analysis : summarization, correlation, and visualization, Mirkin, B., 2019

Data analysis and machine learning