Selected Topics in Data Science
Серебренников Дмитрий Евгеньевич
- The goal of this course is to acquaint students with data science methods and terminology, and to teach them how to implement these methods using R programming language.
- • Knows key machine learning concepts
- Extracts insights from analysis of the models
- • Handles different kinds of data and ML methods
- • Realizes how to implement basic ML models in R
- • Understands how social scientists use ML tools
- Topic 1: Intro to Data Science.
- Topic 2: Unsupervised Learning: Dimension Reduction.
- Topic 3: Introduction to Supervised Learning: Regression and Regularization.
- Topic 4: Ensemble methods.
- Topic 5: Introduction to Deep Learning.
- Topic 6: Interpretable Machine Learning (IML) / Explainable AI (XAI).
- Topic 7: Data Harvasting.
- Five Homeworks50%. Students will have 5 homeworks during the course. You will have to write them using Rmarkdown and send a html file via email to course instructor. Each homework will grade from 1 to 10 and bring an appropriate amount of percentage to the final grade.
- QuizzesDuring the course you will take 6 quizzes. They will be at the first part of the class. Each of them will consist of 5 questions and right answer on a question bring 1% to overall score. Therefore one quiz could bring you 5%.
- Final presentation
- 2022/2023 4th module0.5 * Five Homeworks + 0.1 * Participation + 0.1 * Final presentation + 0.3 * Quizzes
- 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