Course

MICRO-455: Machine learning I

Summary

Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data

Lectures

Instructor

Aude Billard

Expertise RoboticsMachine LearningArtificial IntelligenceHuman-Robot InteractionMechatronicsDynamical Systems ControlComputational Neuroscience Aude Billard is full professor and head of the LASA laboratory at the School of Engineering at the Swiss Institute of Technology Lausanne (EPFL), and the Director of the Swiss Innovation Booster on Robotics. Dr. Billard acts as the President of the IEEE Robotics and Automation Society (RAS), after serving in several roles in the administrative and executive committe

Official source

https://edu.epfl.ch/coursebook/en/machine-learning-i-MICRO-455

Moodle Page

http://go.epfl.ch/MICRO-455

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Summary

Lectures

Instructor

Aude Billard

Official source

https://edu.epfl.ch/coursebook/en/machine-learning-i-MICRO-455

Moodle Page

http://go.epfl.ch/MICRO-455

About this result

Lectures in this course (60)

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Pitfalls and Caveats in Machine Learning

Covers challenges in machine learning, emphasizing the importance of choosing relevant data and algorithms.

Pitfalls and Caveats in Machine Learning

Covers the importance of data and algorithms in machine learning and how to avoid noise.

Principal Component Analysis: Dimensionality Reduction

Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.

PCA: Intuition

Covers the basics of PCA, exercises on dimensionality reduction, and criteria for choosing projections.

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