Fundamentals of Statistical Pattern Recognition

EE-612 at EPFL: fundamental tools of machine learning and pattern recognition, from basics to deep learning, for Ph.D. students. Given biennially from 2013 to 2023.

This course (EE-612, listed in the EPFL coursebook) presents fundamental tools used in machine learning and pattern recognition, ranging from the most basic to more advanced (logistic regression, PCA, LDA, multi-layer perceptrons, deep learning, Gaussian mixture models, and support vector machines). It is given to post-graduate (Ph.D.) students at the École Polytechnique Fédérale de Lausanne, Switzerland, and can serve as a prerequisite for more advanced courses on machine learning.

It ran six times, biennially in odd years, from 2013 to 2023.

Program

• Credits: 4 ECTS (100–120 working hours)
• Grading: lab assignments (40%) and final exam (60%)
• Prerequisites: linear algebra, probability and statistics, Python
• Topics: linear and logistic regression, k-nearest neighbours, decision trees, boosting, dimensionality reduction (PCA, LDA, t-SNE), probability distribution modelling, and neural networks / deep learning.

Cover: EPFL Rolex Learning Center by Fridolin freudenfett, resized, CC BY-SA 4.0.