The course provides a practical introduction into machine learning methods with the focus on deep learning. 

Lectures with practical sessions and a student's project applying machine learning to a practical problem.

Introduction to AI and machine learningMachine learning preliminariesBasic ML approaches Artificial Neural NetworksDeep Learning ArchitecturesApplications

The course assumes the basic prior knowledge of the probability theory, linear algebra, and optimization methods. Knowledge of Python programming language is a plus.

Basic knowledge of linear algebra, probability theory, and calculus

Course books:Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning  Cambridge: MIT press. (Available online: https://www.deeplearningbook.org/)Aston Zhang A., Lipton, Z.C., Li M., & Smola A.J. Dive into Deep Learning (2020) (Available online: https://d2l.ai/)Extra literature for beginners:James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. SpringerRaschka, S. (2015). Python machine learning. Packt Publishing Ltd.Extra literature - classics:Mitchell, T. (1997) Machine Learning. McGraw Hill. (a bit old, but still the best intro to ML for computer scientists)Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning.  2nd edition, Springer.Please consider visiting also the practical course 623.625 "Machine Learning and Deep Learning" of Pierre Tassel, which provides an introduction to various aspects of programming deep neural networks with PyTorch.

Design and implementation of a project applying deep learning to solve a selected problem 

Students should demonstrate their ability to apply deep learning methods discussed in the course.

Grades are given based on a project proposal, a mid-term review presentation, and a final report.