Кафедра прикладной математики и информатики (Нижний Новгород)

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Публикации

Книга

From Approximate Variation to Pointwise Selection Principles

Vyacheslav V. Chistyakov.

Springer, 2021.

Статья

Combinatorics and Algorithms for Quasi-Chain Graphs
В печати

Alecu B., Atminas A., Lozin V. V. et al.

Algorithmica. 2022. P. 1-23.

Глава в книге

Faster exploration of some temporal graphs

Adamson D., Gusev V. V., Malyshev D. et al.

In bk.: 1st Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2022, March 28–30, 2022, Virtual Conference). Vol. 221. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, 2022. Ch. 5. P. 5:1-5:10.

Препринт

The approximate variation of univariate uniform space valued functions and pointwise selection principles

Vyacheslav V. Chistyakov, Svetlana A. Chistyakova.

Functional Analysis. arXiv [math.FA]. Cornell University, NY, USA, 2020. No. 2010.11410.

Все публикации

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

Статус:: Курс обязательный

Когда читается:: 1-й курс, 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;
Student will be able to process tabular data
Apply dimensionality reduction to data
Apply regularization to address overfitting
Distinguish components of learning error
Perform cluster analysis
Successfully passed final assesment and TBD
Train and validate decision tree models for classification
Train and validate KNN model
Train and validate linear models for classification and regression problems
Train ensembles of models
Train MLP for supervised learning taks
Tune decision tree hyperparameters
Understand core principles of artificial neural networks

Course Contents

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

Assessment Elements

Tests (1-6 weeks)
Programming Assignment (2 per course)
Final project - applied practical problem to solve
Staff graded assignment

Interim Assessment

2021/2022 2nd module
0.4 * Final project - applied practical problem to solve + 0.4 * Programming Assignment (2 per course) + 0.2 * Tests (1-6 weeks)

Bibliography

Recommended Core Bibliography

Computer vision : models, learning, and inference, Prince, S. J. D., 2012
Core data analysis : summarization, correlation, and visualization, Mirkin, B., 2019
Introduction to machine learning, Alpaydin, E., 2020
Machine learning : a probabilistic perspective, Murphy, K. P., 2012
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

Pattern recognition and machine learning, Bishop, C. M., 2006
Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017
Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1667293

Modern Methods of Data Analysis