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Similarity search inspired by fruit flies; A stock prediction architecture
Lightning talk: Alan Akbik "Intro to Flair - Open Source NLP Framework" ---- Talk 1: Drosophila hits Machine Learning - A new algorithm for similarity search derived from the olfactory processing of fruit flies Speaker: Dr. Daniela Schmidt Bio: Daniela is a biologist who did her PhD in the field of insect olfaction and chemical ecology. Interested by statistical analysis at that time she left academia to detect her passion for machine learning five years ago. Since then she had diverse projects like modelling solvency prediction, document classification, information extraction and image recognition. She has a broad and general interest in diverse ML topics. Currently she is working as a data scientist in the big data research group of the Adolf Würth GmbH & Co. KG. Abstract: Only recently, ML specialists have detected how brains learn to smell as a source of inspiration for developing new methods. In this talk, I will introduce you to a new and promising algorithm for the nearest neighbor problem which was inspired by the olfactory processing of the fruit fly Drosophila. It was published last November in Science by Sanjoy Dasgupta, Charles F. Stevens and Saket Navlakha (http://science.sciencemag.org/content/358/6364/...­). I will give you an introduction to the approximate nearest neighbor problem and how it can be solved by a special family of hash functions: locality sensitive hashing (LSH). Then I will explain how insect process odor information and how they tag odors by a sparse random projection. This sparse random projection represents a new type of LSH-function and confronts with the dense Gaussian projection in traditional LSH. Moreover, the fly-LSH has been proven to be faster and more accurate than traditional LSH. ---- Talk 2: The Architecture of a Stock Prediction System Speakers: Stefan Savev & Rey Farhan Abstract: In this talk, we will share our experience of building a stock prediction system based on the recently released Deutsche Börse Public Dataset (https://registry.opendata.aws/deutsche-boerse-pds/). Architecture components include the following. 1) achieving insights about stock market behavior via the available data and validating existing predictive approaches; 2) encoding ML models with a Domain Specific Language (DSL) that targets explicitly the properties of financial data; 3) encoding trading strategies based on the results of the ML model using another DSL. We combine approaches from data science, engineering, and stock market traders. We believe is a rare open source attempt to use ML for stock prediction in combination with a strategy and which evaluates the predictions reliably. Code: https://github.com/Originate/dbg-pds-tensorflow-demo https://github.com/Originate/dbg-pds-tensorflow-demo/blob/ss-add-strategy-image/ROADMAP.md.

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Rudi-Dutschke-Straße 23 · Berlin

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A meetup for academics, professionals and hobbyists interested in applications and latest developments in Machine Learning, and AI more broadly. We talk about:

• Computer vision, speech recognition, text mining, generative design

• New papers that we're excited about, and software that we use

• Cool applications of AI & machine learning, and how we made them

We strive to focus on the science & technology side, as opposed to the commercial side.

We typically meet the first Monday of every month.

We're always looking for interesting presentations. If you have a topic you want to talk about, anything from 10 to 45 minutes long, then please email gtrent@gmail.com. For talks we are explicitly *not* commercial. We organize this meetup because we are passionate about AI & ML, not to promote some product or service.

If an organization would like to host us, or sponsor food & drink, let us know.

Our official Twitter hashtag is #MLBerlin (https://twitter.com/search?q=%23MLBerlin).

VISIT US AT: http://machinelearning.berlin/

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