Магистратура
2023/2024
Современные методы анализа данных: машинное обучение
Статус:
Курс обязательный (Магистр по наукам о данных)
Направление:
01.04.02. Прикладная математика и информатика
Где читается:
Факультет компьютерных наук
Когда читается:
2-й курс, 1 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
52
Охват аудитории:
для своего кампуса
Прогр. обучения:
Магистр по наукам о данных (заочная)
Язык:
английский
Кредиты:
3
Контактные часы:
10
Course Syllabus
Abstract
In this course, students are introduced to the main types of machine learning problems. Simple and multiple linear regression, evaluation of the quality of the obtained models, interpretation of the importance of features are considered. The solution of the classification problem is also discussed: algorithms such as Logistic Regression, Support Vector Machine and Decision Trees are considered. In addition, students gain insight into model ensembles.
Learning Objectives
- Get familiar with the basic machine learning definitions
- Understand such concepts as overfitting and regularization
- Understand how gradient descent works and how it is used in machine learning
- Know which models are used to solve regression and classification tasks
- Be able to use the scikit-learn library to train machine learning models
Expected Learning Outcomes
- Get familiar with the basic machine learning definitions
- Understand how gradient descent works and how it is used in machine learning.
- Understand such concepts as overfitting and regularization.
- Know which models are used to solve regression and classification tasks.
- Be able to use the scikit-learn library to train machine learning models.
Course Contents
- 1. Introduction to Machine Leaning
- 2. Linear Regression and Gradient Descent
- 3. Overfitting and Regularization
- 4. Classification
- 5. Decision Trees
- 6. Ensembling