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Обычная версия сайта
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.

Modern Methods of Data Analysis

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
8
Кредиты

Преподаватель

Гречихин Иван Сергеевич

Гречихин Иван Сергеевич

Course 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

Learning Objectives

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

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

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

Assessment Elements

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

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

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