Data Analysis and Machine Learning

2022/2023

Учебный год

ENG

Обучение ведется на английском языке

6

Кредиты

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

Казаков Максим Анатольевич

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

• 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