EH172-7-SL-CO:
Machine Learning for Social Scientists
2022/23
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer & Long Vacation
Postgraduate: Level 7
Current
Monday 24 April 2023
Wednesday 04 October 2023
15
03 February 2023
Requisites for this module
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This course introduces methods of machine learning for social scientists. The broad objective of machine learning is to uncover patterns in data, either as an exploratory device or to make predictions. The course covers a variety of topics, including supervised, unsupervised, and ensemble learning. We discuss how the general principles of machine learning, as well as specific algorithms. The choice of technique, as well as application and interpretation take center stage in the course. Specific algorithms that will be dis-cussed include artificial neural networks, bagging, boosting, classification and regression trees, clustering, decision rules, k-nearest neighbors, principal components, probabilistic learning, random forests, regression, and support vector machines. General principles include cross-validation, global and local interpretation, loss functions, optimization, regularization, variable importance, and feature selection.
No information available.
Machine learning is of ever greater importance in the social sciences, both inside and outside of academia. The ultimate goal of this course is to make you conversant with the most important techniques and ideas of machine learning. This means that you have a good overview of the fields and its relevance for social scientific research. It also means that you have sufficient background knowledge to allow you to study further. This is important because 2-week course can only scratch the surface of machine learning, which evolves quickly. Being conversant with machine learning also means that you understand how to implement these methods, which we shall do in R. Note that the examples will be relatively small, with an eye on minimizing computation time. Where necessary, we shall discuss how to engage in big data analysis.
Course Prerequisites
This is an introductory course, meaning that prior familiarity with machine learning is not expected. It is useful if you have used the linear regression model before, as it is a starting point for much of the course. A basic knowledge of probability theory is indispensable, as is a working understanding of R. In R, you should know: (1) how to access various data sources; (2) the basic objects of the language; (3) basic operations; (4) the ability to compute descriptive statistics and create graphs; and (5) the basics of tidyverse.
Required text – (this text will be provided by ESS):
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning with Applications in R. New York: Springer. ISBN: 978-1-4614-7138-7.
Background texts
For a cursory introduction to many of the topics, you might consult: Lantz, Brett. 2019. Machine Learning with R: Expert Techniques for Predictive Modeling. Packt Publishing, 3rd. edition.
For an introduction to statistical concepts and R, you might want to consult Learning Statistics with R.
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
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Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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