A WHOLE-DAY MEETUP OF THE MUNICH DATAGEEKS

Abstract: Deep Learning and the Industries

Deep Learning is difficult (?) and there are no hard and fast rules for designing a deep network. However, there are certain practices one can follow and these will come out only after dealing with many different datasets and trying out different methods. This talk will focus on the current deep learning applications on a broad level with their impact on the industries. We will cover the most popular deep learning libraries and how to tackle different deep learning problems. The talk will end with a focus on how deep learning is being used by the current startup companies and if companies are really selling "deep-learning".

Bio:

Abhishek Thakur is chief data scientist at Boost AI. His focus is more toward applied machine learning and deep learning rather than theoretical. He completed his Masters in Computer Science in University of Bonn in early 2014 and since then has been working in the industries with a research focus on Automatic Machine Learning. He likes taking part in machine learning competitions and has attained a worldwide rank of 3 on the popular website Kaggle. He has also performed well on different other machine learning competition platforms.

Abstract: Word Embeddings - the Good, the Bad, and the Ugly

Word embeddings are the new magic tool for natural language processing. Without cumbersome preprocessing and feature design they are able to capture the semantics of language and texts, simply by being fed with lots of data. So they say.

We applied word embeddings - and for that matter also sentence embeddings - to various problem domains, such as chatbots, car reviews, news and language learning all in German domain-specific corpora. We will share our experiences and learnings: how much feature design was necessary, which alternative approaches are available and for which applications we were able to make use of word embeddings (recommendations, topic detection, error correction)?

Bio: Fabian Dill, CEO, DieProduktMacher GmbH
Fabian is Co-founder and CEO of DieProduktMacher GmbH in Munich, Germany. Before founding DieProduktMacher, Fabian served as Head of Business Performance at a subsidiary of Hubert Burda Media. He also co-founded a machine learning startup (KNIME) in 2006. Fabian has many years of experience building online products, seeing them fail and succeed. The rise of conversational interfaces with chatbots and voice user interfaces led his way again into the fields of Machine Learning and Natural Language Processing.

Abstract: Content-As-A-Service

This talk is centered around building real-time and lightening responsive news search engine. From streaming data processing to system architecture, all bits and pieces will be covered. Specially in case of news articles streaming in the system, challenges and whole ecosystems of aggregation to scoring algorithm will be introduced. Realisation of plug and play micro-service architecture will be another focus of this talk so that in the end audience will see how complex but fun is building news search engine. This talk will be accompanied by live demo showing how to create one focused news feed using Cliqz News Technology.

Bio:

Heeren Sharma is Software Engineer in News Team at Cliqz GmbH. He completed his Masters  in Computer Science at TU Munich. During writing his Masters thesis, interest of developing data-centric products sparked in him. And, real game changer came while working at Cliqz GmbH being a fresh university graduate. Since past 3 years, developing the products and services which are more oriented towards streaming data pipelines (specially for News Search), inference engines and fast responsive systems, Heeren has developed keen interest in data engineering roles i.e. from ideation to POC to production ready systems. A problem-solver by nature, he's particularly interested in Information Retrieval and Machine Learning. Most of the time, he likes to munch in Python and use Elasticsearch as Thor’s hammer.

Abstract: How data literate is your audience?

Data in the hands of a few data experts can be powerful, but data at the fingertips of many is what will be truly transformational. It is crucial to increase data access for business experts, but is getting the data out really all that we have to do to make digital transformation happen?

The vast majority of people are increasingly unprepared to navigate today’s world of data. But you want your audience to be data literate, that means to have the ability to read, work with, analyze and argue with data.

But how can you know the recipient’s level of expertise to make sure that your message will be received? Opening your organization’s data to all employees (or even the public) will only make sense if everyone has at least a basic level of data literacy.

From my experience as a data visualization designer, most of the time, the data literacy level of the audience is a black box. This is a huge pain, so together with my friends at Cavorit and the HTW Berlin, I have decided to find ways to easily measure the skill level of an audience. I want to share our learnings about this complex skill, the prerequisites and subskills involved, and how the skill levels are distributed among a population. These insights can help to adjust the complexity of visualizations for each audience, make training programs more effective, raise awareness and hopefully close the data literacy gap.

Bio:

Evelyn Münster is a Data Visualization Designer at Cavorit and is also working as a freelancer. She is a multidisciplinary thinker on the crossroads of data analysis, data visualization and UX design. Having a background in media art and software development, she has been developing interactive data visualization tools for science and business since 2008. She writes a data viz newsletter in German and is active on Twitter as @dataviz_de.

Abstract: Make hyperparameters great again

While tuning hyperparameters of machine learning algorithms is computationally expensive, it also proves vital for improving their predictive performance. Methods for tuning range from manual search to more complex procedures like Bayesian optimization. This talk will demonstrate the latest methods for finding good hyperparameter-sets within a set period of time for common algorithms like xgboost.

Bio:

Daniel Kühn is a catastrophe analyst at Guy Carpenter, one of the world’s largest reinsurance brokers. In his work he uses state of the art probabilistic models to estimate the economic damage caused by large scale natural disasters. He also is a part time PhD student at the department for computational statics at LMU, where he focuses his research on hyperparameter optimization and automatic machine learning.

Abstract: Transfer Learning for Fun and Profit

Transfer learning is exciting because it unlocks solutions that weren't feasible a few years ago. In fact, choices to compose from pre-trained models for computer vision tasks became abundant. In this talk, we will explore how to make these choices for image classification and feature extraction.
The analysis is inspired by practical use-cases where human supervision and compute time is often limited. The results are presented for two datasets across PyTorch’s model-zoo. First, a toy dataset where scale invariance is important. Second, a dataset from an object detection pipeline where rotation invariance is important. Lastly, we will cover the human success factors of such a project.

Bio:

Alexander Hirner is industrial engineer with the conviction to make humans smarter with computers and vice versa. He developed machine learning solutions for unstructured data while studying in Silicon Valley and Europe. In 2014 he founded Ethereum Vienna, a blockchain 2.0 meetup. His ongoing research interest in frictionless data-markets is at the intersection those two technologies.

Abstract: Time series shootout - ARIMA vs. LSTM

Bio

Sigrid Keydana is a data scientist with the DACH-based IT consulting company Trivadis.
In the field of data science and machine learning, she focuses on deep learning (concepts and frameworks), statistical learning and statistics, natural language processing and software development using R.
She has a broad background in software development (esp. Java and functional programming languages like Scheme and Haskell), database administration, IT architecture and performance optimization.
She writes a blog (http://recurrentnull.wordpress.com) and is active on Twitter as @zkajdan.

Abstract: From Pokémon to Donald Trump - Mining and Visualizing weird stuff

In his talk, Markus talks about extracting, analyzing and visualizing data from unusual sources. He will talk about two of his projects: First he'll talk about using a Pokémon Go bot to gather and processing data on 250k spawns of Pokémon in Munich during the peak of the Pokémon Go hype in 2016 and the insights about the logic behind the game that he gained by visualizing this data. Second he will talk about his analysis of 16 mio. comments in r/The_Donald, a community on Reddit that is devoted to Donald Trump, analyzing (among others) the community's language compared to natural english, activity levels over time and geographical distribution of users.

Bio

After working as an IT Consultant for several years, Markus is now a department head at German VoD Service maxdome. During the day he is responsible for a all client- / partnerfacing APIs and the services behind. At night however, when his secret passion comes out, he searches for yet untapped data to mine and visualize.

Abstract: My Robot can learn - using Reinforcement Learning to teach my Robot

A new star is rising at the machine learning horizon: reinforcement learning (RL). The concept entails an agent and an incentive-based training system. The agent learns via incentives and improves its behaviour - a self-learning system using simple rules - leading to artificial intelligence (AI). The talk covers an introduction to reinforcement learning and its combination with deep learning to achieve an AI system - a smart, intelligent bot! Annotating data to create a base model and its refinement through RL mechanisms brings us to the next level. Let’s build our self-learning robot!

Bio:

Marcel is working as a Program Manager at Microsoft AI & Research in Germany. In the past his work was focused on data and high-scale systems. Nowadays, his focus is towards natural user interfaces and speech recognition. Thus, this brings a nice combination of a smart way of handling data and on the other side understanding interaction with humans and robots in a smart way. Besides this he enjoys giving talks, writing papers, do some coding and electronic experiments.

Abstract: My Robot can learn - using Reinforcement Learning to teach my Robot

A new star is rising at the machine learning horizon: reinforcement learning (RL). The concept entails an agent and an incentive-based training system. The agent learns via incentives and improves its behaviour - a self-learning system using simple rules - leading to artificial intelligence (AI). The talk covers an introduction to reinforcement learning and its combination with deep learning to achieve an AI system - a smart, intelligent bot! Annotating data to create a base model and its refinement through RL mechanisms brings us to the next level. Let’s build our self-learning robot!

Bio:

Olivia Klose (@oliviaklose) is a Software Development Engineer in the Commercial Software Engineering group at Microsoft. She is focussing on all analytics services on Microsoft Azure, in particular Hadoop (HDInsight), Spark and Machine Learning, and is a frequent speaker at German and international conferences, such as TechEd Europe, PASS Summit and Technical Summit. Prior to joining Microsoft, she studied Computer Science with Mathematics at the University of Cambridge, the Technical University of Munich and IIT Bombay. Here, she focussed on Machine Learning in Medical Imaging.