Machine Learning & Deep Learning & Stats
20 talks from the 2024 edition.
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A conceptual and practical introduction to Hilbert Space Gaussian Process (HSGP) approximation methods ▶ Recording 📝 Transcript
Dr. Juan Orduz
In this talk, we explore a new method to approximate Gaussian processes using spectral analysis methods, known as the Hilbert Space Gaussian process (HSGP) approximation. This technique allows us to use and fit...
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Breaking AI Boundaries: Fairness Metrics in Unstructured Data Domains ▶ Recording 📝 Transcript
Daniel Klitzke
This presentation addresses the rare use of machine learning fairness metrics in domains with indirect human impact, e.g., automotive engineering. We briefly map out the space of use cases to examine the necessity,...
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Build TikTok's Personalized Real-Time Recommendation System in Python with Hopsworks ▶ Recording 📝 Transcript
Aleksey Veresov
The real-time recommendations engine in Tiktok, Monolith, is so good it has been described as "digital crack" (by Andrej Karpathy, former head of AI at Tesla). In this tutorial, we will build the core components of...
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Content Recommendation with Graphs: From Basic Walks to Neural Networks ▶ Recording 📝 Transcript
Dr. Mirza Klimenta
Discover how graph algorithms are transforming content recommendation in this insightful talk. We'll journey from the basics of graph-based models, exploring simple graph walks, to the cutting-edge realm of Graph...
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Everything you need to know about change-point detection 📝 Transcript
Charles Truong
Change-point detection is a crucial processing step when dealing with long and non-stationary time series. It has been applied in many contexts, such as human activity recognition, speech/sound processing and...
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From idea to production in a day: Leveraging Azure ML and Streamlit to build and user test machine learning ideas quickly ▶ Recording 📝 Transcript
Florian Roscheck
Getting a machine learning solution in front of users usually takes some time. The data science tech stack is full of time traps and infrastructure issues might slow down deployment. The Azure Machine Learning...
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Lessons learned from deploying Machine Learning in an old-fashioned heavy industry ▶ Recording 📝 Transcript
Robert Meyer
About 5 years ago my co-founder and I launched alcemy, a Machine Learning startup to help decarbonize the cement and concrete supply chain. I experienced first hand moving from a simple proof of concept, a ML model...
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Machine Learning on microcontrollers using MicroPython and emlearn ▶ Recording 📝 Transcript
Jon Nordby
This presentation will show you how to deploy machine learning models to affordable microcontroller-based systems - using the Python that you already know. Combined with sensors, such as microphone, accelerometer or...
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Missing Data, Bayesian Imputation and People Analytics with PyMC ▶ Recording 📝 Transcript
Nathaniel Forde
We demonstrate a range of different approaches to missing data imputation in employee engagement survey data. Contrasting frequentist style full-information maximum likelihood approaches with more direct Bayesian...
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Moving from Offline to Online Machine Learning with River ▶ Recording 📝 Transcript
Tun Shwe
The foundations of machine learning were built on offline batch processing techniques for model training and inference. As organisations become more dependent on real-time data, the technological trend for machine...
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Personalizing Carousel Ranking on Wolt's Discovery Page: A Hierarchical Multi-Armed Bandit Approach ▶ Recording 📝 Transcript
Marcel Kurovski, Steffen Klempau
Wolt's Discovery page serves as the primary gateway for millions of weekly users exploring diverse cuisines and products. With over 130,000 merchants in 25 countries, presenting relevant content poses a unique...
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Reinforcement Learning: Bridging The Gap Between Research and Applications ▶ Recording 📝 Transcript
Michael Panchenko
Reinforcement learning (RL) has great potential for industrial applications, but few mature software frameworks exist to facilitate its use. This talk discusses efforts to improve the software landscape for RL,...
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Select ML from Databases ▶ Recording 📝 Transcript
Gregor Bauer
This talk introduces a new workflow for building your machine learning models using the capabilities of modern databases that support machine learning use cases natively. There is an overview of how machine learning...
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Tackling the Cold Start Challenge in Demand Forecasting ▶ Recording 📝 Transcript
Alexander Meier, Daria Mokrytska
In this talk, we address the Cold Start problem in Demand Forecasting, focusing on scenarios where historical data is scarce or nonexistent. This constitutes a common situation in practice, such as with the launch of...
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Tailored and Trending: Key learnings from 3 years of news recommendations
Dr. Christian Leschinski
Every day, we engage with news, and more often, these are curated by recommendation engines. Building such an algorithm poses some unique challenges, different from movie or product recommendations: articles have a...
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That’s it?! Dealing with unexpected data problems ▶ Recording 📝 Transcript
Simon Pressler
Drawing on experience with multiple consulting projects, this talk shares experiences on how to deal with unexpected data problems. We are discussing how fare purely technical solutions as well as domain knowledge...
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The evolution of Feature Stores ▶ Recording 📝 Transcript
Olamilekan Wahab
Feature Stores have become an important component of the machine learning lifecycle. They have been particularly pivotal in bridging the gap between data engineering and machine learning workflows(experimentation,...
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Using ML to find out the "Why"? A Tutorial in Causal Machine Learning
Oliver Schacht, Jan Teichert-Kluge
Machine learning is mostly used for predicting outcome variables. But in many cases, we are interested in causal questions: Why do customers churn? What is the effect of a price change on sales? How can we optimize...
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Your Model _Probably_ Memorized the Training Data ▶ Recording 📝 Transcript
Katharine Jarmul
I know you probably don't want to hear about it, but your deep learning model probably memorized some of its training data. In this talk, we'll review active research on deep learning and memorization, particularly...
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🌳 The taller the tree, the harder the fall. Determining tree height from space using Deep Learning and very high resolution satellite imagery 🛰️ ▶ Recording 📝 Transcript
Ferdinand Schenck
A case study of how we use Deep Learning based photogrammetry to calculate the height of trees from very high resolution satellite imagery. We show the substantial improvement achieved by switching from classical...