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Обычная версия сайта
17
Февраль

Modern Methods of Data Analysis

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

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
  • Train and validate KNN model
  • Train and validate decision tree models for classification
  • Tune decision tree hyperparameters
  • Distinguish components of learning error
  • Train ensembles of models
  • Train and validate linear models for classification and regression problems
  • Apply regularization to address overfitting
  • Apply dimensionality reduction to data
  • Perform cluster analysis
  • Understand core principles of artificial neural networks
  • Train MLP for supervised learning taks
  • Successfully passed final assesment and TBD
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

  • Interim assessment (2 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