APPLYING AI & MACHINE LEARNING
TO MEDIA AND ENTERTAINMENT
LOS ANGELES, DECEMBER 14 2017
The Data Science Salon is a destination conference which brings together specialists face-to-face to educate each other, illuminate best practices, and innovate new solutions in a casual atmosphere with food, drinks, and entertainment.
Data Science Salon unites the brightest leaders in the media and entertainment in Los Angeles data science fields. We gather to educate each other, illuminate best practices, and innovate new solutions. Data Science Salon | LA is a one day conference with workshops for executives, data scientists, developers, and business development professionals alike. We’ve collected extensive data to figure out just the right mix of people, content, and entertainment to make our conferences as seamless, informative, and fun as possible.
Raise your profile and establish thought leadership for your business. Join the most influential leaders in data science and share your knowledge about the latest machine learning techniques and interesting case studies. Data Science Salon will bring together twenty speakers per event. With a full day of diverse content there’s plenty of opportunities to share your knowledge, attract new hires and make lifelong business connections.
Coffee & Registration
Come early and get a book of your choice from O’Reilly Media (first come first serve)
Welcome from GA
Setting the Day's Intention
Clustering YouTube: A Top Down and Bottom up Approach
At ZEFR we know that when an advertisement on YouTube is relevant to the content a user is watching it is a better experience for both the user and the advertiser. In order to facilitate this experience we discover billions of videos on YouTube and cluster them into concepts that advertisers and brands want to buy to align with their particular creatives.
To serve our clients we use two different clustering strategies, a top down supervised learning approach and a bottom up unsupervised learning approach. The top down approach involves using human annotated data and a very fast and robust machine learning model deployment system that solves problems with model drift.
Our clients are also interested in discovering topics on YouTube. To serve this need we use unsupervised clustering of videos to surface clusters that are relevant. This type of clustering allows ZEFR to highlight what users are currently interested in. We show how using Latent Dirichlet Allocation can help to solve this problem. Along the way we will show some of the tricks that produce an accurate unsupervised learning system.
This talk will touch on some common machine learning engines including Keras, TensorFlow, and Vowpal Wabbit. We will also briefly touch on our open source Scala DSL for model representation, Aloha.Jonathan Morra, Vice President, Data Science at ZEFR
Deep Learning as a Product @ Scribd
I’ll cover our experience using deep learning, going from scratch to deploying models in production to improve the product experience. I’ll go in-depth in terms of how we started deep learning from scratch, including navigating the maze of frameworks and hyper-parameters to optimize. I’ll discuss pitfalls of using other people’s algorithms and make a call for more rigor in publishing data science blog posts. I’ll close with how our failure turned into an open source contribution and the work in moving from dev to production.Kevin Perko, Data Science Lead at Scribd
Opening Keynote: A Timely Discussion on How Content, Platform, and User Data have Forever Altered the Landscape of the Media Industry
Xavier Kochhar sits down with Leonard ArmatoXavier Kochhar, Founder of The Video Genome Project, Hulu & Leonard Armato, CEO & Tour Commissioner at AVP Pro Beach Volleyball Tour, Inc.
Content Science at Netflix
Lunch, Music & Networking
Fireside Chat: Data Science Applications in Media and Entertainment
Joe Devon (Moderator) Founding Partner atDiamond sits down with: Alejandro Cantarero VP, Data at tronLos Angles Times Media Group, Keisuke Inoue VP Data Science at Emogi, Gwen Miller, Vice President, Audience & Platforms at Kin Community, Hollie Choi, Executive Director, IT Intellectual Property Management at 20th Century Fox. For a deep conversation about Data Science Applications in Media and Entertainment.Alejandro Cantarero VP, Data at Los Angeles Times, Keisuke Inoue VP Data Science at Emogi, Gwen Miller, VP, Audience & Platforms at Kin Community, Hollie Choi, Executive Director, IT Intellectual Property Management at 20th Century Fox
A Journey of Deploying a Data Science Engine to Production
Challenges of Improving Performance on Android
Making Big Data Smart
Within marketing research, big data is often described as being “census” data for the population that it represents. The devil is in the details and when we take a closer look we can see that this isn’t the case. There are many situations that are not captured within the population that big data purports to be a census of. Big data isn’t even a census of itself since it’s not uncommon for records to be excluded either by accident during the collection process or by design in the cleaning processor. Unfortunately, our industry is so enamored with the size of big data that some users of data are willing to trade off precision for tonnage. Fortunately, if the shortcomings of big data are understood and corrected it can accurately represent the population that it measures in the correct proportion to the universe. We will discuss a method that Nielsen has developed called “Common Homes” that is designed to identify and correct the shortcomings of big data sets that represent media consumption.Daniel Monistere, SVP – Client Solutions, Nielsen
Coffee & Snack Break
Expect a special suprise during this break =)
Key Note Address: The Age of Co-Creation™
⚡️ Building Smart AI: How Deep Learning Can Get You Into Deep Trouble
Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.Michael Housman, Co-Founder and Chief Data Science Officer, RapportBoostAI
⚡️ Content Metrics at BuzzFeed
Data Science Slam Poetry
Stick around for more networking accompanied by artisanal pizza, beer and wine and LIVE Entertainment!
WHAT PEOPLE ARE SAYING
Past Events Gallery
check our previous conference photos
- General Assembly Downtown LA, 360 E 2nd St, Los Angeles
- (415) 322-0760
- December 14, 2017
- 9:30 AM – 8:00 PM