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On Trees with a Given Diameter and the Extremal Number of Distance-k Independent Sets

D. S. Taletskii.

Journal of Applied and Industrial Mathematics. 2023. Vol. 17. No. 3. P. 664-677.

Глава в книге
Uncertainty of Graph Clustering in Correlation Block Model

Artem Aroslankin, Valeriy Kalyagin.

In bk.: Mathematical Optimization Theory and Operations Research: Recent Trends. 22nd International Conference, MOTOR 2023, Ekaterinburg, Russia, July 2–8, 2023, Revised Selected Papers, vol 1881. Springer, 2023. P. 353-356.

Independent sets versus 4-dominating sets in outerplanar graphs

Taletskii D.

math. arXiv. Cornell University, 2023

Data Analysis and Machine Learning

Учебный год
Обучение ведется на английском языке


Course Syllabus


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
  • 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

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

Assessment Elements

  • non-blocking test
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    1 * Exam


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