Course

CIVIL-226: Introduction to machine learning for engineers

Summary

Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinking education at EPFL, Civil students will learn ML with civil case studies (summary generated by ML)

Lectures

Instructor

Olga Fink

Teaching & PhD PhD Students Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei Past EPFL PhD Students Ismail Nejjar, Mengjie Zhao Courses Data Science for infrastructure condition monitoring CIVIL-332 The course will cover the relevant steps of data-driven infrastructure condition monitoring, starting from data acquisition, going through the steps pre-processing of real

Official source

https://edu.epfl.ch/coursebook/en/introduction-to-machine-learning-for-engineers-CIVIL-226

Moodle Page

http://go.epfl.ch/CIVIL-226

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Summary

Lectures

Instructor

Olga Fink

Official source

https://edu.epfl.ch/coursebook/en/introduction-to-machine-learning-for-engineers-CIVIL-226

Moodle Page

http://go.epfl.ch/CIVIL-226

About this result

Lectures in this course (12)

Introduction to Python: Basics and Anaconda Setup

Covers the basics of Python programming and setting up Anaconda and Jupyter Notebook.

Machine Learning Basics

Covers the basics of machine learning for engineers, including grading, course requirements, practical examples, AI concepts, and ML applications.

Ground Penetrating Radar: Data Analysis

Explores the automated picking of reinforcement bars within Ground Penetrating Radar data using machine learning and signal processing techniques.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Introduction to Machine Learning

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

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