This one-semester course provides a first introduction to machine learning including the following topics:
- Linear regression
- Logistic regression
- Support vector machines
- Deep Neural network
- Dimensionality reduction
- Anomaly detection
complemented by a exercise session of practical Matlab exercises.
None, if taken as a master course. If taken as an advanced course in the bachelor program:
- basic math courses offered in our bachelor programs
- algorithms and data structures
- introduction to computer science including programming
- have an insight of the fundamentals of Machine Learning
- have the ability to design your own machine learning algorithm to solve some specific problems with Matlab
- know how to improve the performance using training data
- Christopher M.. Bishop. Pattern recognition and machine learning. Springer, 2006.
- Alpaydin, Ethem. Introduction to machine learning. MIT press, 2014.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep learning. MIT press, 2016