CA$49 – CA$349

Premium Hands-on Workshop: Recommender Systems

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Event description
Recommenders show up in every industry, and most of the techniques that are used in recommender show up in many other places within ML

About this Event

Workshop Overview

As most industries benefit for a better understanding from their market, it is natural to optimize what products should be offered to which user. This is the heart of recommender systems, having an understanding of the user. This interaction between user and product makes recommenders appear in every industry, product offering, ranking, and everything that involves a choice by the user can be understood as a recommender system. In this workshop we will cover the basics of recommender systems, the most common techniques used in production, and some of the techniques that will probably be part of their future. The format is hands on, relying heavy in theory and latest developments, as well as simplified implementations.

Important Dates

Early Bird Deadline: October 17, 2019

Refund Deadline: October 18, 2019

Sales end on: October 16, 2019

Please note that this workshop will happen on 3 separate evenings:

Session 1: October 30, 2019

Session 2: November 6, 2019

Session 3: November 13, 2019

Why you should attend

In this 3-session intensive workshop, we will bring you up to speed with everything needed to build a strong background in Recommender Systems.

This workshop is built on the instructors extensive experience in academia and industry on related topics.

This workshop is the first in its series and paves the way theoretically and technically for many application specific workshops to follow.

Join in-person or online

Watch and rewatch the video recordings as often as you need during the 3 weeks of the workshop, and interact with other participants and TAs on our Slack

Need more information?

Send us any questions you might have (events@ai.science), or

Join us for a Q&A session for an opportunity to hear from the instructor and ask them questions about the workshop: https://recsyse-lnl.eventbrite.ca

Target Audience

Data Scientists, Machine Learning Engineers, Students, AI Researchers

Prerequisites

  • Very comfortable with Python, and familiarity with Pytorch (Some basic Java knowledge is useful)
  • Environment: google colab
  • Theoretical knowledge assumed: Math, basic of ML and DL

Learning Outcomes

In this workshop you will learn the basics of recommenders and the short-comings of the current models, and will get familiar with how deep learning shows up within the recommenders framework. (This is subject to change)

  • Weighted matrix factorization. (Netflix dataset)
  • Ranking. (Spotify)
  • Deep learning and recommenders.(Movielens)

Pre-workshop reading material

This will be updated in the next couple weeks.

  • Weighted matrix factorization names. https://developers.google.com/machine-learning/recommendation/collaborative/matrix
  • PageRank, BPR, and rank metrics https://www.link-assistant.com/news/google-page-rank-2019.html
  • Auto-encoders and recommenders https://medium.com/@connectwithghosh/recommender-system-on-the-movielens-using-an-autoencoder-using-tensorflow-in-python-f13d3e8d600d

Learning Material

All online and in-person participants will have access to the following learning material:

  • Slides from the sessions
  • Hands on notebooks
  • Video recording of the sessions (you can use the videos to watch the parts that you missed, or re-watch any parts that are still unclear for you; access to videos beyond one week after the workshop is available to be purchased; see tickets >> add-ons)

Instructor

Felipe Perez

Machine Learning Research Scientist, Layer 6 AI

Felipe Perez is a M.L. researcher at Layer 6 AI working in a variety of fields including recommenders, NLP, and DL architectures. Previous to this he was a Scientist working at ZGL where he helped create DL models that could help offerings. He finished his Ph.D. in Mathematics at the University of Michigan.

Course Modules

The workshop happens on 3 evenings, and covers the following topics:

Day 1: The basics of recommenders. Weighted Matrix factorization, why and how?

  • Basic approaches to recommender systems; collaborative filtering, base content, k-nn, etc.
  • Weighted matrix factorization (WMF) and the reason why it may improve the performance of the models.
  • Hands on WMF and the training approaches

Day 2: Ranking

  • Ranking and different metrics associated with it
  • Frequently used ranking algorithms
  • State of the art ranking approaches, BPR in deep learning.

Day 3: Auto-encoders in Recommenders

  • Auto-encoders as a compression technique.
  • Clustering in the latent space.
  • Augmenting WMF, the auto-encoder approach for recommendations
  • Is deep learning the right approach for recommenders?

█░ Kick-starter style

Please note that our technical workshops are run kick-starter style in the sense that they will only happen if a certain amount of funds are raised by the 2-week deadline prior to the event (Oct 17, 2019):

Funds raised so far to enable the workshop:

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Learning Packages

You can customize your learning package. Click on tickets, select your base package (in-person or online), and then tailor your experience the way you want using add-ons.

There is early bird public discount starting at 40% and decreasing by 10% every week until the end of early bird on Oct 17th, 2019

Discount Codes

There are discount codes available

  • AISC members receive discounts according to their participation credit; refer to the slack channel for more detail

Referral Program

AISC Members, have personalized referral discount codes up to 50% that they can share with their friends and coworkers. Each code can be used 3 times. In order to ensure that people are using the code with your permission they need to enter your email address when registering

Non-members, can enjoy a group discount (10% on top of the current early bird discount) by forming groups of 3+ and registering together. So, go talk to your friends or coworkers right now so that you all save 10% more together.

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Eventbrite's fee is nonrefundable.

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