Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Building Efficient Learning Algorithms: A Computational Regularization Perspective - Lorenzo Rosasco

Alan Turing Institute via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the theoretical foundations of machine learning algorithms in this 45-minute talk by Lorenzo Rosasco at the Alan Turing Institute. Delve into the intersection of statistics, probability, and optimization to understand how algorithmic design choices impact learning outcomes. Examine a least squares learning scenario to discover how various algorithmic techniques can be unified within a regularization framework. Gain insights into building resource-efficient and accurate algorithms, moving beyond traditional empirical trial-and-error approaches. Learn how computational regularization offers a new perspective on algorithm design, potentially revolutionizing the way we approach machine learning problems.

Syllabus

Building efficient learning algorithms: a computational regularization perspective - Lorenzo Rosasco

Taught by

Alan Turing Institute

Reviews

Start your review of Building Efficient Learning Algorithms: A Computational Regularization Perspective - Lorenzo Rosasco

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.