Geek Girls Carrots Zurich #11: Cybersecurity & Machine Learning for Recommendations

When: Thu, May 31, 2018; 6:30 PM – 9:30 PM

Where:  Impact Hub Zürich – Viadukt, 93 Viaduktstrasse, 8005 Zürich, Switzerland

Tickets: https://www.eventbrite.com/e/carrots-zurich-11-tickets-45738790955?aff=efbevent

Agenda:

18:30 – Introduction

18:45 – Talk by Muharem & Sumeyya Hrnjadovic “Security has escaped the IT department and is entering your life .. but don’t panic :)”

19:15 – Networking

19:35 – Talk by Katarina Kovac “Product Recommendation and the Models behind them”

20:05 – Networking

NOTE: The online registration is required to attend this event.

“Security has escaped the IT department and is entering your life .. but don’t panic :)”

IT has spread to power and control devices we use on a daily basis without even giving it a second thought: cars, baby monitors, fitness armbands, kitchen appliances, building controls etc.

IT is still a young and immature discipline, however, and while it brings convenience and new possibilities, its unavoidable flaws and bugs also expose us to unprecedented and serious threats. In our talk we will present a few cautionary tales and teach you some basic security principles to help you master this new reality.

About Muharem Hrnjadovic:

Software engineer specializing in crypto-finance (blockchains etc.), security and distributed systems development. I mostly hack in Python 🙂

 

 

 

 

About Sumeyya Hrnjadovic:

Aspiring hacker, learning Python and getting the hang of navigating the weird and wondrous universe of IT.

“Product Recommendation and the Models behind them”

You have all come across product recommendation when looking for a specific product on online shopping sites like Amazon, selecting a movie on Netflix or watching a video on YouTube. Behind the displayed suggestions are powerful statistical and machine learning techniques applied to a large volume of data related to product information and user properties and behaviour. In this talk Katarina will explain how the product recommendation engines work and showcase some product recommendation usecases from the finance industry.

About Katarina Kovac:

Katarina is a senior data scientist with more than 15 years of experience working on data-related projects in both academia and industry. Prior to being employed in the finance industry, she was a researcher in observational-data astrophysics. In her daily work, Katarina employs various machine learning algorithms and develops new tools to find patterns and generate meaningful insights and compelling stories from raw data. The tools which she uses include programming languages such as Python, R, and C, and tools such as Spark, Hive and SQL amongst others.

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