Машинное обучение

2022/2023

Учебный год

RUS

Обучение ведется на русском языке

5

Кредиты

Кто читает:

Вечерне-заочный факультет экономики и управления (Пермь)

Статус:

Курс по выбору

Когда читается:

4-й курс, 3 модуль

Программа дисциплины

Аннотация

The present program of educational discipline establishes requirements to educational results and learning outcomes of the student and determines the content and types of training sessions and reporting. The program is intended for the teachers conducting discipline; "Machine Learning", educational assistants and students of a direction of preparation 38.04.05 Business informatics, studying under the educational program Information analytics in enterprise management;

Цель освоения дисциплины

• strengthening the skills of work in the language of Python, knowledge and understanding of the tasks of data management, including data loading, data conversion, and preliminary analysis and visualization of data
• familiarity with the main tasks and models of machine learning, knowledge of methods of evaluation of the quality of work of various models of machine learning
• understanding the process of integration of machine learning models within the tasks facing potential customers

Планируемые результаты обучения

• Can formalize a business task as a machine learning task, can solve it and evaluate its quality
• It sets and solves the classification problem.
• It sets and solves the problem of regression.
• It sets and solves the task of clustering.
• It sets and solves the task of identifying the effect of the impact.
• Knows the methods of machine learning.

Содержание учебной дисциплины

• Section 1: Classification and regression objective
• Section 2: Objective of impact assessment
• Section 3: The challenge of building recommendation systems
• Section 4: Tasks of learning without a teacher
• Section 5: Deep learning models
• The section. 6. Working on the project

Элементы контроля

• аудиторная работа
• самостоятельная работа
• экзамен

Промежуточная аттестация

• 2022/2023 учебный год 3 модуль
  0.4 * экзамен + 0.3 * аудиторная работа + 0.3 * самостоятельная работа

Список литературы

Рекомендуемая основная литература

• Hall, M., Witten, Ian H., Frank, E. Data Mining: practical machine learning tools and techniques. – 2011. – 664 pp.

Рекомендуемая дополнительная литература

• Haroon, D. (2017). Python Machine Learning Case Studies : Five Case Studies for the Data Scientist. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1623520