Бакалавриат
2022/2023
Машинное обучение
Статус:
Курс по выбору (Социология и социальная информатика)
Направление:
39.03.01. Социология
Кто читает:
Департамент социологии
Где читается:
Санкт-Петербургская школа социальных наук
Когда читается:
4-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Кольцов Сергей Николаевич
Язык:
английский
Кредиты:
5
Контактные часы:
48
Course Syllabus
Abstract
Machine Learning belongs to the family of disciplines related to the analysis of real data, based on the application of mathematical statistics, data analysis, pattern recognition, extraction of patterns from data. In this course classical algorithms of machine learning based on tabular, textual data and images are consideredfrom the point of view of applicability of models for sociological analysis. This course is aimed at students with no or minimal programming skills. In this course, students gain an understanding of the possibility of data analysis, structuring digital data formats, methods and tools for statistical, social analysis based on machine learning algorithms.The course is implemented on the basis of the software tool 'Orange data mining' (https://orangedatamining.com/), in which the creation of the research scheme is reduced to visual programming. This course is implemented for two modules. The students' reporting consists of the following. In each module, students must present a small research project (in the form of an oral presentation and research workflow based on the passed material). At the end of the course there will be an exam on all passed material.
Learning Objectives
- Learn algorithms and their main advantages and limitations for social science goals
- Obtain skills to work with machine learning software / codes
- Be able to work with different types of data, such as textual/tabular data and images.
Expected Learning Outcomes
- Analyze data with machine learning tools
- Do textual preprocessing (lemmatization and tokenization)
- Present the resulting project in terms of machine learning
- Visualize results of the analysis
- Analyze textual, numerical data and images
Assessment Elements
- ExamThe exam consists of 3 practical tasks, for which students will have to demonstrate knowledge of machine learning algorithms and the ability to use them in solving practical tasks. The student must present the solution to the problems in the form of a research diagram, performed in the program 'Orange data mining'. The student's work will be evaluated on the basis of the solutions presented in exams.If questions arise from the teacher, the student should explain the operation of the individual parts of the diagrams presented.
- Presentation projectThe presentation project in the first and second modules are a small creative-scientific investigation using machine learning models. The research should be presented in English. The presentation should include the goals and objectives of the project, a description of the models used, and the results obtained (see details in ‘Presentation Requirement’).
Interim Assessment
- 2022/2023 2nd moduleCumulative evaluation = 0.3*Presentation_project(1 module) + 0.3*Presentation_project(2 module) Final score = cumulative evaluation + 0.4*Exam. The presentation projcet in the first and second modules are a small creative-scientific investigation using machine learning models. The research should be presented in English. The presentation should include the goals and objectives of the project, a description of the models used, and the results obtained (see details in ‘Presentation Requirement’).
Bibliography
Recommended Core Bibliography
- Miroslav Kubat. (2017). An Introduction to Machine Learning (Vol. 2nd ed. 2017). Springer.
Recommended Additional Bibliography
- Črt Gorup, Mitar Milutinovič, Matija Polajnar, Marko Toplak, & Lan Umek. (n.d.). Orange: Data Mining Toolbox in Python. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.59267479