The course will address the following topics: Supervised learning: linear and nonlinear models for regression and prediction, statistical theory of learning, feature extraction and model selection. Deep learning: architectures including autoencoders, convolutional neural networks and learning procedures. Model performance assessment: cross validation, k-fold cross validation, leave-one-out, bootstrap. Unsupervised learning: K-means clustering, fuzzy C-means, principal component analysis.
Students will learn how to design linear and nonlinear models for regression, prediction and classification as well as assess their performance in a sound way. At the same time, students will learn how to use deep learning architectures and learning algorithms in key real-world applications.
Lab sessions will focus on practical aspects and show how to design an appropriate machine learning solution to real-world problems. Basics in Calculus, Probability and Statics are requested
Assignments and exam.
- Gori, Marco. Machine learning: a constraint-based approach. Cambridge, MA: Morgan Kaufmann Publishers, 2018.
- Hastie, Trevor J., Tibshirani, Robert, Friedman, Jerome. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. [corrected at 5th printing]. New York, N.Y.: Springer, 2011.
- James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert. An introduction to statistical learning: with applications in R. Second edition. New York: Springer, 2021.
- Bachelor of Science in Informatics, Lecture, Elective, 3rd year
- Master of Science in Economics, Lecture, 120 ECTS, Elective, 2nd year
- Master of Science in Economics, Lecture, Internship or Electives, Elective, 2nd year
- Master of Science in Economics in Finance, Lecture, 1st year