The objective of this course is to provide the student with the fundamental knowledge and skills in the field of modern machine learning.
At the end of the course and having completed the essential reading and activities students should be able to:
- develop an understanding of the process to learn from data
- be familiar with a wide variety of algorithmic and model based methods to extract information from data
- apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation
- gain knowledge and experience on conducting machine learning in Python
- Basic programming experience with Python
- Basic concepts of linear algebra: vectors and matrices and operations on them
- Single and multivariate calculus topics such as derivatives and integrals
- Key notions of probability and statistics
- James G., Witten D., Hastie T., Tibshirani R. and J. Taylor. An introduction to Statistical Learning: with Applications in Python (2023).
- Bishop C.M. Pattern recognition and machine learning. Springer (2006)
- Murphy K.P. Machine learning: A probabilistic perspective. MIT Press, second edition (2012)