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.
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.
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.
- Linear Algebra
- Probability & Statistics
- Programming Fundamentals 1
- Software Atelier 1: Fundamentals of Informatics
- T.Hastie, R.Tibshirani, J.Friedman, The elements of statistical learning, Springer
- Slides and material provided by the professor.