↓ Skip to main content
DataScience.LA
Data Science Meetup - January 13: Data Science @ Datapop

Data Science Meetup - January 13: Data Science @ Datapop

·1 min
Meetups Machine Learning / Data Science data science machine learning meetups marketing R&D
Author
Author
Szilard Pafka

Data Science @ Datapop: The Marketing Knowledge Graph

presented by Dr. Ben Zamanzadeh, CTO at DataPop

Today’s eCommerce has evolved into a complex multi-dimensional optimization problem. DataPop’s semantic technology and Marketing Knowledge Graph is used to generate Omni-Channel advertising campaigns. This presentation will review our technology stack, machine learning models, discuss what worked what did not work for us and do a live demo of Marketing Knowledge Graph.

Dr. Ben Zamanzadeh is passionate about innovation, machine learning and solving challenging R&D problems. Previously he served as VPE at LeadPoint and led Yahoo’s Domain Search and R&D teams. Ben has experience in search, ad tech, eCommerce, financial, mobile, data science, machine learning and semantic computing. Ben holds a Ph.D. in Computer Science from the UCLA where he serves on the CS Advisory Board.

Date: January 13, 2015

Timeline:

  • 6:30pm food/drinks and networking
  • 7:30pm talk starts promptly

You must have a confirmed RSVP and please arrive by 7:25pm the latest. Please RSVP here on Eventbrite.

Venue: Cross Campus in Santa Monica will kindly host this meetup, and Factual will kindly sponsor this meetup, providing food+drinks.

Related

Meetup: No-Bullshit Data Science

Meetup: No-Bullshit Data Science

·2 mins
Meetups Machine Learning / Data Science data science machine learning meetups big data
Update: Slides here: Slides: https://speakerdeck.com/szilard/machine-learning-meetup-febr-2017 Our next meetup: No-Bullshit Data Science by Szilard Pafka Abstract: While extracting business value from data has been performed by practitioners for decades, the last several years have seen an unprecedented amount of hype in this field. This hype has created not only unrealistic expectations in results, but also glamour in the usage of the newest tools assumably capable of extraordinary feats. In this talk I will apply the much needed methods of critical thinking and quantitative measurements (that data scientists are supposed to use daily in solving problems for their companies) to assess the capabilities of the most widely used software tools for data science. I will discuss in details two such analyses, one concerning the size of datasets used for analytics and the other one regarding the performance of machine learning software used for supervised learning.