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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

Instructors (2)

Alexandre Massoud Alahi

Alexandre Alahi is currently an Assistant Professor at EPFL. He spent five years at Stanford University as a Post-doc and Research Scientist after obtaining his Ph.D. from EPFL. His research enables machines to perceive the world and make decisions in the context of transportation problems and smart environments. He has worked on the theoretical challenges and practical applications of socially-aware Artificial Intelligence, i.e., systems equipped with perception and social intelligence. He was awarded the Swiss NSF early and advanced researcher grants for his work on predicting human social behavior. He won the CVPR Open Source Award (2012) for his work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for his sparsity-driven algorithm that has tracked more than 100 million pedestrians to date. His research has been covered internationally by BBC, abc, PBS, Euronews, Wall street journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.

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

Summary

Lectures

Instructors (2)

Alexandre Massoud Alahi

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

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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