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Premium Hands-on Workshop: Modern Recipes for Anomaly/Novelty Detection

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Modern Recipes for Anomaly/Novelty Detection

About this Event

Workshop Overview

In this workshop we will introduce contemporary techniques for outlier detection. And we will try to have a balance of theory and hand of experience here. We will review some of the statistical and classical approaches and we will learn about most of the new deep learning based techniques that can be used for this task. At the beginning of the workshop we will introduce some benchmark datasets (like credit card fraud dataset) and while learning new techniques, we will apply them and we will see their results.

Important Dates

Early Bird Deadline: January 9, 2020

Refund Deadline: January 9, 2020

Sales end on: January 15, 2020

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

Session 1: January 16, 2020

Session 2: January 23, 2020

Session 3: January 30, 2020

"Why should I care about Anomaly/Novelty Detection?"

Detecting outliers or anomalies is one of the core problems in Machine Learning and it has recently become an active research topic with the exploding growth of big data and AI techniques in lots of applications, like intrusion detection, fraud detection, medical and public health, Image processing, novelty detection in text data and so on. Detecting anomalies can help in predicting accidents in traffic patterns for autonomous driving cars. It can help in predicting rare diseases in healthcare. It can also be used to identify bottlenecks in network infrastructure. Or it can be simply used to clean our data and improve our model performance.

“How can I attend?”

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://ai-anom-lnl.eventbrite.ca

Target Audience

Data Scientists, Machine Learning Engineers, Students, AI Researchers, Software Engineers

Prerequisites

  • Very comfortable with Python, and familiar with Keras
  • Environment: google colab [we prefer to use this to avoid env setup issues]
  • Theoretical knowledge assumed: It is expected that the participants are already familiar with the basic statistics, ML, neural network

Learning Outcomes

You will learn about different approaches for anomaly and novelty detection. You will learn about generative models, unsupervised, semi-supervised and self supervised techniques for anomaly detection. Specifically, you will be learning to:

  • build reliable fraud detection models
  • detect outlier in image data
  • build modern generative models (like AE, VAE, AAE ..)

Pre-workshop reading material

  • Chandola, V., Banerjee, A. & Kumar, V., 2009. Anomaly Detection: A Survey. ACM Computing Surveys, July. 41(3).
  • Goldstein, M. & Uchida, S., 2016. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLoS ONE, 11(4), p. 31.
  • Outlier Analysis by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. [Preview.pdf]

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

Rohollah Soltani

Machine Learning Researcher, Knowtions Research Inc.

Machine learning scientist with 8 years of blended industrial and academic experience in machine learning, deep learning, representation learning and natural language processing. Experienced in design, implement and deploy of machine learning models in finance and health care.

Course Modules

The workshop happens on 3 evenings, and covers the following topics: (This is subject to change)

Day 1: Introduction

  • Introduction and context
  • Statistical approaches
  • Distance-Based Anomaly Detection Approaches
  • Testing some of the statistical methods on benchmark dataset

Day 2: Model-Based Anomaly Detection Approaches

  • Classical ML models for anomaly detection
  • Semi-supervised and self-supervised approaches
  • Clustering-Based Anomaly Detection Approaches
  • Testing some of the classical and self-supervised methods on benchmark dataset

Day 3: Deep Learning Anomaly Detection Approaches

  • Deep generative models (AE, VAE, GAN..)
  • Testing some of the Deep generative methods on benchmark dataset

█░ 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 (January 9, 2020):

Funds raised so far to enable the workshop:

░░░░░░░░░░ 0%

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 November 14th, 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 (around 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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