| Study program / study programs: Advanced data analytics |
| Course: Models of Statistical Learning |
| Teacher(s): Bulajić Milica, Vukmirović Dragan, Radojičić Zoran, Marković Aleksandar, Jeremić Veljko, Ignjatović Marina, Maričić Milica, Zornić Nikola, Mutavdžić Dragosav |
| Course status: Elective |
| ECTS points: 10 |
| Prerequisites: none |
| Course objective: Acquiring the ability to employ advanced models of statistical learning, to interpret the obtained results, and the ability to recognize the model of statistical learning suitable for solving the given problem. Mastering the usage of advanced features of modern statistical and simulation software. |
| Learning outcomes: After completing the course, students will acquire the experience in understanding the concepts of advanced models of statistical learning in contemporary statistical and simulation software and the experience needed for their application in real-word business problems. |
| Course structure and content: The concepts and techniques of models of statistical learning are first introduced through the review and presentation of theoretical foundations, followed by practical work using contemporary statistical and simulation software. Regression models. Logistics regression. Lasso regression. Polynomial regression. Classification trees Resampling methods. Cross validation. Jackknife. Parametric and nonparametric bootstrap. Supervised learning. Unsupervised learning. Support Vector Machines (SVM). Application of Monte Carlo simulation in advanced data analytics. Agent based-simulation – description of agent behavior using the models of statistical learning. Implementation of the covered methods and models in contemporary statistical and simulation software. |
| Literature/Readings: Maričić, M., Ignjatović, M. & Jeremić, V. (2022). Models of statistical learning (in Serbian). Akademska misao, Beograd. ISBN: 978-86-7466-925-9 Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning – Data Mining, Inference, and Prediction, Springer. Available online: https://www.springer.com/gp/book/9780387848570 James, G., Witten, D., Hastie, T., Tibshirani, R. (2016). An Introduction to Statistical Learning with Applications in R, Springer. Available online: https://www.springer.com/gp/book/9781461471370 Bulajić M., Jeremić, V., Radojičić, Z. (2012). Advance in Multivariate Data Analysis – Contributions to Multivariate Data Analysis, Faculty of Organizational Sciences Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press. https://mitpress.mit.edu/books/introduction-agent-based-modeling |
| The number of class hours per week: Lectures: 4 Labs: 0 Workshops: 1 Research study: 2 Other classes: 0 |
| Teaching methods: Individual and group work; lectures and labs |
| Evaluation/Grading (maximum 100 points): Pre-exam requirements (Project): 60 Final exam (Oral exam): 40 |
