- 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 or relational data
- Analyze data with machine learning tools
- Analyze textual and numerical data
- Do textual preprocessing (lemmatization and tokenization)
- Present the resulting project in terms of machine learning
- Visualize results of the analysis
- Topic 1. Introduction to machine learning.
- Topic 2. Overview of mathematical formalism necessary for understanding of machine learning.
- Topic 3. Data preprocessing.
- Topic 4. Regression (overview models).
- Topic 5. Feature selection.
- Topic 6. Cluster analysis (Kmeans, Cmeans, Hierarchical clustering).
- Topic 7. Linear models of classification and regressions.
- Topic 8. KNN and SVM classification.
- Topic 9. Naïve Bayes classifier.
- Topic 10. Topic modeling.
- Topic 11. Decision trees.
- Presentation_projectAn project is a written self-study on a topic offered by the teacher or by the student him/herself approved by teacher. The topic for project includes development of skills for critical thinking and written argumentation of ideas. An project should include clear statement of a research problem; include an analysis of the problem by using concepts and analytical tools within the subject that generalize the point of view of the author
- Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.
- Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968
- A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62