#machine learning

Couldn't make it to ML Conference 2019 in Berlin? No problem!

Data to the Rescue! Predicting and Preventing Accidents at Sea – Livestream of the ML Con Keynote

In the second keynote at ML Conference 2019 in Berlin, Dr. Yonit Hoffman dives into the topic of data science: How can data science and machine learning help prevent accidents at sea that cost lives, money and environmental destruction? Here’s the livestream of the keynote if you couldn’t make it to Berlin this year.

Couldn't make it to ML Conference 2019 in Berlin? No problem!

Robots and Ethics in the Digital Age – Livestreaming of the ML Con Keynote

Welcome to this year’s ML Conference and Voice Conference in Berlin! Keynote speaker Dr. Janina Loh explores the moral questions that go hand in hand with the construction and use of robots—from a critical overview of some fields of robotics to practical implications. Watch our live stream here if you couldn’t make it to Berlin this year.

Watch Vadim Markovtsev's ML Conference session

Mining software development history: Approaches and challenges

This talk from the Machine Learning Conference gives a fun history of mining examples and presents some of the available tooling. Some of the topics we’ll be going over include embeddings, dynamic time warping, seriation, and HDBSCAN. Watch Vadim Markovtsev’s ML Conference session and come away knowing more about software development.

Personalizing city experiences

Artificial intelligence & machine learning: The brain of a smart city

In this article, explore how a combination of artificial intelligence and machine learning can act as the brains of a smart city while simultaneously considering how a smart city experience can become more personalized without compromising the privacy of its residents. Read on to see what the advantages and disadvantages of an ML and AI-powered smart city are.

Watch Christoph Windheuser's ML Conference session

How to implement chatbots in an industrial context

Chatbots are among the most popular applications of artificial intelligence, machine learning, and natural language processing, and many people are already familiar with them. Various companies are developing first prototypes to improve their customer communication and support functions. How do we begin to implement them into an industrial context?