Rapid developments of social networking sites, online media and other internet-generated data are making machine learning an essential analytical tool of social scientists and industrial analysts of social data. Nowadays, social researchers should not only be able to work with different types of data, such as textual or relational data, but should also have skills to interpret results obtained with complex mathematical algorithms. In this course, students will first get to know basic machine learning algorithms and their main advantages and limitations for social science goals. Second, they will obtain skills to work with machine learning software / codes. Third, by the end of the course all students will produce small-scale research project that may be used in their Master theses. This course focuses on the use of machine learning algorithms in the python and Jupyter Notebook.
Present the resulting project in terms of machine learning
Topic 2. Overview of mathematical formalism necessary for understanding of machine learning.
Overview of the mathematical model of machine learning. Overview of basic concepts from the field of linear algebra. Overview of elements of mathematical analysis. Overview of Jupiter notebook, the general principles of working with notebook.
Topic 3. Data preprocessing.
Vector model of text collections. Discussion of the data cleansing process, text lematization, stop words removal. Implementing the preprocessing process Python (Jupiter notebook) for Russian and English languages. Discussion of the lematization procedures: Mystem. 2. Pymorphy. 3. NLTK.
non-blocking Presentation_project
0.7 * Homework + 0.3 * Presentation_project
Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.