Explore the limitations and failure cases of popular machine learning algorithms in this conference talk from GOTO Berlin 2019. Dive into live demonstrations showcasing unexpected outcomes from linear regression to cutting-edge deep learning techniques. Gain insights into practical examples where treating AI as a black box can lead to unintended consequences. Discover the importance of understanding algorithm limitations in various fields, including social problems, medicine, and data science. Learn why critical thinking and domain expertise remain crucial when implementing AI solutions, even as machine learning becomes more accessible through APIs and pre-built models.
Overview
Syllabus
Introduction
Machine Learning
Linear Regression
Decision Trees
Deep Learning
Social Problems
Medicine
Data
My favorite example
Data Science
Taught by
GOTO Conferences
Related Courses
-
AI skills for Engineers: Supervised Machine Learning
-
AI and Machine Learning Foundations - Python, Data Science, and MLOps
-
Classical Machine Learning for Financial Engineering
-
Machine Learning Course for Beginners
-
Data Science Hands-On Crash Course
-
Foundations and Applications of Machine Learning (Bengali)
