Advanced Machine Learning: Non-Linear Models

This course equips participants with the architecture, implementation, and evaluation skills needed to work with non-linear classifiers and regressors. From k-nearest neighbors and ensemble methods to support vector machines and post-modeling diagnostics, participants develop the technical depth to tackle complex supervised learning challenges with confidence.

While Advanced Machine Learning: Non-Linear Models can be taken as a standalone course, it is also part of EPFL’s learning pathway in applied machine learning. It is the third of four Microcredentials leading to the Certificate of Open Studies (COS) in Applied Machine Learning https://www.formation-continue-unil-epfl.ch/formation/applied-machine-learning/, providing a flexible pathway toward an EPFL credential in applied machine learning.

Upon successful completion of this course, participants will be able to:

Implement k-nearest neighbors as a non-parametric modelling technique and optimise its hyperparameters
Build ensemble architectures based on bagged and boosted decision trees to minimise variance and bias
Train support vector machines for complex linear and non-linear supervised learning tasks
Execute post-modelling diagnostic analysis using advanced performance metrics and permutation importance
Address modelling challenges such as multilabel classification, the curse of dimensionality, and dimensionality reduction

Nongraded

Data Analysts and Quantitative Analysts looking to bridge the gap to machine learning; Research Scientists who need advanced, custom predictive frameworks; and Software Engineers transitioning into the machine learning and AI space to build real-world applications.